I will present our studies including diffractive imaging with coded diffraction and imaging through opaque wall based on machine learning.

One-shot digital holographic imaging has the advantages of high stability and low temporal cost. However, the reconstruction is affected by the speckle noise. Resampling ring-mask method in spectrum domain is proposed for speckle reduction. The useful spectrum of one hologram is divided into several sub-spectra by ring masks. In the reconstruction, angular spectrum transform is applied to guarantee the calculation accuracy which has no approximation. N reconstructed amplitude images are calculated from the corresponding sub-spectra. Thanks to speckle’s random distribution, superimposing these N uncorrelated amplitude images would lead to a final reconstructed image with lower speckle noise. Normalized relative standard deviation values of the reconstructed image are used to evaluate the reduction of speckle. Effect of the method on the spatial resolution of the reconstructed image is also quantitatively evaluated. Experimental and simulation results prove the feasibility and effectiveness of the proposed method.

Lensfree holography provides a new way to effectively bypass the intrinsical trade-off between the spatial resolution and field-of-view (FOV) of conventional lens-based microscopes. Unfortunately, due to the limited sensor pixel-size, unpredictable disturbance during image acquisition, and sub-optimum solution to the phase retrieval problem, typical lensfree microscopes only produce compromised imaging quality in terms of lateral resolution and signal-to-noise ratio (SNR). In this paper, we propose an adaptive pixel-super-resolved lensfree imaging (APLI) method to address the pixel aliasing problem by Z-scanning only, without resorting to subpixel shifting or beam-angle manipulation. Furthermore, an automatic positional error correction algorithm and adaptive relaxation strategy are introduced to enhance the robustness and SNR of reconstruction significantly. Based on APLI, we perform full-FOV reconstruction of a USAF resolution target across a wide imaging area of ~29.85 mm² and achieve half-pitch lateral resolution of 770 nm, surpassing 2.17 times of the theoretical Nyquist-Shannon sampling resolution limit imposed by the sensor pixel-size (1.67 μm). Full-FOV imaging result of a typical dicot root is also provided to demonstrate its promising potential applications in biologic imaging.

Optical imaging through turbid media and around corner is a difficult challenge. Even a very thin layer of a turbid media, which randomly scatters the probe light, can appear opaque and hide any objects behind it. Despite many recent advances, no current method can image the object behind turbid media with single record using coherent laser illumination. Here we report a method that allows non-invasive single-shot optical imaging through turbid media and around corner via speckle correlation. Instead of being as an obstacle in forming diffractionlimited images, speckle actually can be a carrier that encodes sufficient information to imaging through visually opaque layers. Optical imaging through turbid media and around corner is experimentally demonstrated using traditional imaging system with the aid of iterative phase retrieval algorithm. Our method require neither scan of illumination nor two-arm interferometry or long-time exposure in acquisition, which has new implications in optical sensing through common obscurants such as fog, smoke and haze.

With the rapid development of China's space industry, digitization and intelligent is the tendency of the future. This report is present a foundation research about guidance system which based on the HSV color space. With the help of these research which will help to design the automatic navigation and parking system for the frock transport car and the infrared lamp homogeneity intelligent test equipment.

The drive mode, steer mode as well as the navigation method was selected. In consideration of the practicability, it was determined to use the front-wheel-steering chassis. The steering mechanism was controlled by the stepping motors, and it is guided by Machine Vision. The optimization and calibration of the steering mechanism was made. A mathematical model was built and the objective functions was constructed for the steering mechanism.

The extraction method of the steering line was studied and the motion controller was designed and optimized. The theory of HSV, RGB color space and analysis of the testing result will be discussed

Using the function library OPENCV on the Linux system to fulfill the camera calibration. Based on the HSV color space to design the guidance algorithm.

In recent years, many optical systems have been developed for securing information, and optical encryption/encoding has attracted more and more attention due to the marked advantages, such as parallel processing and multiple-dimensional characteristics. In this paper, an optical security method is presented based on pure phase encoding with biometric information. Biometric information (such as fingerprint) is employed as security keys rather than plaintext used in conventional optical security systems, and multiple-stage phase-encoding-based optical systems are designed for generating several phase-only masks with biometric information. Subsequently, the extracted phase-only masks are further used in an optical setup for encoding an input image (i.e., plaintext). Numerical simulations are conducted to illustrate the validity, and the results demonstrate that high flexibility and high security can be achieved.

Coherent Anti-Stokes Raman Scattering (CARS) microscopy has attracted lots of attention because of the advantages, such as noninvasive, label-free, chemical specificity, intrinsic three-dimension spatial resolution and so on. However, the temporal overlap of pump and Stokes has not been solved owing to the ultrafast optical pulse used in CARS microscopy. We combine interference spectrum of residual pump in Stokes path and nonlinear Schrodinger equation (NLSE) to realize the temporal overlap of pump pulse and Stokes pulse. At first, based on the interference spectrum of pump pulse and residual pump in Stokes path, the optical delay is defined when optical path difference between pump path and Stokes path is zero. Then the relative optical delay between Stokes pulse and residual pump in PCF can be calculated by NLSE. According to the spectrum interference and NLSE, temporal overlap of pump pulse and Stokes pulse will be realized easily and the imaging speed will be improved in CARS microscopy.

To realize portable device with high contrast imaging capability, we designed a quantitative phase microscope using transport of intensity equation method based on a smartphone. The whole system employs an objective and an eyepiece as imaging system and a cost-effective LED as illumination source. A 3-D printed cradle is used to align these components. Images of different focal planes are captured by manual focusing, followed by calculation of sample phase via a self-developed Android application. To validate its accuracy, we first tested the device by measuring a random phase plate with known phases, and then red blood cell smear, Pap smear, broad bean epidermis sections and monocot root were also measured to show its performance. Owing to its advantages as accuracy, high-contrast, cost-effective and portability, the portable smartphone based quantitative phase microscope is a promising tool which can be future adopted in remote healthcare and medical diagnosis.

As a lensfree imaging technique, ptychographic iterative engine (PIE) method can provide both quantitative sample amplitude and phase distributions avoiding aberration. However, it requires field of view (FoV) scanning often relying on mechanical translation, which not only slows down measuring speed, but also introduces mechanical errors decreasing both resolution and accuracy in retrieved information. In order to achieve high-accurate quantitative imaging with fast speed, digital micromirror device (DMD) is adopted in PIE for large FoV scanning controlled by on/off state coding by DMD. Measurements were implemented using biological samples as well as USAF resolution target, proving high resolution in quantitative imaging using the proposed system. Considering its fast and accurate imaging capability, it is believed the DMD based PIE technique provides a potential solution for medical observation and measurements.

Eye tracker is a new apparatus of human-computer interaction, which has caught much attention in recent years. Eye tracking technology is to obtain the current subject’s "visual attention (gaze)” direction by using mechanical, electronic, optical, image processing and other means of detection. While the mapping function is one of the key technology of the image processing, and is also the determination of the accuracy of the whole eye tracker system. In this paper, we present a new mapping model based on the relationship among the eyes, the camera and the screen that the eye gazed. Firstly, according to the geometrical relationship among the eyes, the camera and the screen, the framework of mapping function between the pupil center and the screen coordinate is constructed. Secondly, in order to simplify the vectors inversion of the mapping function, the coordinate of the eyes, the camera and screen was modeled by the coaxial model systems. In order to verify the mapping function, corresponding experiment was implemented. It is also compared with the traditional quadratic polynomial function. And the results show that our approach can improve the accuracy of the determination of the gazing point. Comparing with other methods, this mapping function is simple and valid.

Information of lens aberration of lithographic tools is important as it directly affects the intensity distribution in the image plane. Zernike polynomials are commonly used for a mathematical description of lens aberrations. Due to the advantage of lower cost and easier implementation of tools, image based measurement techniques have been widely used. Lithographic tools are typically partially coherent systems that can be described by a bilinear model, which entails time consuming calculations and does not lend a simple and intuitive relationship between lens aberrations and the resulted images. Previous methods for retrieving lens aberrations in such partially coherent systems involve through-focus image measurements and time-consuming iterative algorithms. In this work, we propose a method for aberration measurement in lithographic tools, which only requires measuring two images of intensity distribution. Two linear formulations are derived in matrix forms that directly relate the measured images to the unknown Zernike coefficients. Consequently, an efficient non-iterative solution is obtained.

Wavefront coding for athermal technique can effectively ensure the stability of the optical system imaging in large temperature range, as well as the advantages of compact structure and low cost. Using simulation method to analyze the properties such as PSF and MTF of wavefront coding athermal system under several typical temperature gradient distributions has directive function to characterize the working state of non-ideal temperature environment, and can effectively realize the system design indicators as well. In this paper, we utilize the interoperability of data between Solidworks and ZEMAX to simplify the traditional process of structure/thermal/optical integrated analysis. Besides, we design and build the optical model and corresponding mechanical model of the infrared imaging wavefront coding athermal system. The axial and radial temperature gradients of different degrees are applied to the whole system by using SolidWorks software, thus the changes of curvature, refractive index and the distance between the lenses are obtained. Then, we import the deformation model to ZEMAX for ray tracing, and obtain the changes of PSF and MTF in optical system. Finally, we discuss and evaluate the consistency of the PSF (MTF) of the wavefront coding athermal system and the image restorability, which provides the basis and reference for the optimal design of the wavefront coding athermal system. The results show that the adaptability of single material infrared wavefront coding athermal system to axial temperature gradient can reach the upper limit of temperature fluctuation of 60°C, which is much higher than that of radial temperature gradient.

The line-of-sight (LOS) stable platform is the most important technology of UAV (unmanned aerial vehicle), which can reduce the effect to imaging quality from vibration and maneuvering of the aircraft. According to the requirement of LOS stability system (inertial and optical-mechanical combined method) and UAV’s structure, a rapid prototype is designed using based on industrial computer using Peripheral Component Interconnect (PCI) and Windows RTX to exchange information. The paper shows the control structure, and circuit system including the inertial stability control circuit with gyro and voice coil motor driven circuit, the optical-mechanical stability control circuit with fast-steering-mirror (FSM) driven circuit and image-deviation-obtained system, outer frame rotary follower, and information-exchange system on PC. Test results show the stability accuracy reaches 5μrad, and prove the effectiveness of the combined line-of-sight stabilization control system, and the real-time rapid prototype runs stable.

With optical micro-scanning technology, the spatial resolution of the thermal microscope imaging system can be increased without reducing the size of the detector unit or increasing the detector dimensions. Due to optical micro-scanning error, the four low-resolution images collected by micro-scanning thermal micro- scope imaging system are not standard down-sampled images. The reconstructed image quality is degraded by the direct image interpolation with error, which influences the performance of the system. Therefore, the technique to reduce the system micro-scanning error need to be studied. Based on micro-scanning technology and combined with new edge directed interpolation(NEDI) algorithm, an error correction technique for the micro-scanning instrument is proposed. Simulations and experiments show that the proposed technique can reduce the optical micro-scanning error, improve the imaging effect of the system and improve the systems spatial resolution. It can be applied to other electro-optical imaging systems to improve their resolution.

The purpose of this work is to develop a method for the accurate reproduction of the spectral colors captured by digital camera. The spectral colors being the purest color in any hue, are difficult to reproduce without distortion on digital devices. In this paper, we attempt to achieve accurate hue reproduction of the spectral colors by focusing on two steps of color correction: the capture of the spectral colors and the color characterization of digital camera. Hence it determines the relationship among the spectral color wavelength, the RGB color space of the digital camera device and the CIEXYZ color space. This study also provides a basis for further studies related to the color spectral reproduction on digital devices. In this paper, methods such as wavelength calibration of the spectral colors and digital camera characterization were utilized. The spectrum was obtained through the grating spectroscopy system. A photo of a clear and reliable primary spectrum was taken by adjusting the relative parameters of the digital camera, from which the RGB values of color spectrum was extracted in 1040 equally-divided locations. Calculated using grating equation and measured by the spectrophotometer, two wavelength values were obtained from each location. The polynomial fitting method for the camera characterization was used to achieve color correction. After wavelength calibration, the maximum error between the two sets of wavelengths is 4.38nm. According to the polynomial fitting method, the average color difference of test samples is 3.76. This has satisfied the application needs of the spectral colors in digital devices such as display and transmission.

The devices with touchscreen are becoming more popular recently; however, most of them suffer from the crucial drawbacks of lacking accurate tactile feedback. A novel electrocutaneous-based tactile device with the name of E-pad is proposed to provide a dynamic and static low-voltage feedback for touchscreen. We optimize the key parameters of the output voltage and design custom-made hardwares to guarantee a comfortable user experience. Users could move their fingers freely across the touchscreen of the proposed device to really feel virtual objects. Two preliminary experiments are conducted to evaluate the interactive performance of the proposed device and the experimental results show that the proposed device can provide a comfortable and distinct tactile feedback.

When sun is used as the light source for atmospheric composition detection, it is necessary to image sun for accurate identification and stable tracking. In the course of 180 second of the occultation, the magnitude of sun light intensity through the atmosphere changes greatly. It is nearly 1100 times illumination change between the maximum atmospheric and the minimum atmospheric. And the process of light change is so severe that 2.9 times per second of light change can be reached. Therefore, it is difficult to control the integration time of sun image camera. In this paper, a novel adaptive integration time control method for occultation is presented. In this method, with the distribution of gray value in the image as the reference variable, and the concepts of speed integral PID control, the integration time adaptive control problem of high frequency imaging. The large dynamic range integration time automatic control in the occultation can be achieved.

A real-time MTFC algorithm of space remote-sensing camera based on FPGA was designed. The algorithm can provide real-time image processing to enhance image clarity when the remote-sensing camera running on-orbit. The image restoration algorithm adopted modular design. The MTF measurement calculation module on-orbit had the function of calculating the edge extension function, line extension function, ESF difference operation, normalization MTF and MTFC parameters. The MTFC image filtering and noise suppression had the function of filtering algorithm and effectively suppressing the noise. The algorithm used System Generator to design the image processing algorithms to simplify the design structure of system and the process redesign. The image gray gradient、dot sharpness、edge contrast and median-high frequency were enhanced. The image SNR after recovery reduced less than 1 dB compared to the original image. The image restoration system can be widely used in various fields.

Plenoptic camera and holography are popular light field measurement techniques. However, the low resolution or the complex apparatus hinders their widespread application. In this paper, we put forward a new light field measurement scheme. The lens is introduced into coherent diffraction imaging to operate an optical transform, extended fractional Fourier transform. Combined with the multi-image phase retrieval algorithm, the scheme is proved to hold several advantages. It gets rid of the support requirement and is much easier to implement while keeping a high resolution by making full use of the detector plane. Also, it is verified that our scheme has a superiority over the direct lens focusing imaging in amplitude measurement accuracy and phase retrieval ability.

An optimized scheme to extract the depth information in the 4D light field data is proposed with the advantage of conciseness. The depth information of a light field scene is estimated according to the disparity of edge pixels. Depths of smooth regions are interpolated after region segmentation. A computer generated light field scene is used as an example to test the validity of our algorithm. We restrict the time complexity of our algorithm to logarithmic linearity, which is available in practical use and is promising in parallel computing.

The surface camera (SCam) of light fields gathers angular sample rays passing through a 3D point. The consistency of SCams is evaluated to estimate the depth map of scene. But the consistency is affected by several limitations such as occlusions or non-Lambertian surfaces. To solve those limitations, the SCam is partitioned into two segments that one of them could satisfy the consistency constraint. The segmentation pattern of SCam is highly related to the texture of spatial patch, so we enforce a mask matching to describe the shape correlation between segments of SCam and spatial patch. To further address the ambiguity in textureless region, a global method with pixel-wise plane label is presented. Plane label inference at each pixel can recover not only depth value but also local geometry structure, that is suitable for light fields with sub-pixel disparities and continuous view variation. Our method is evaluated on public light field datasets and outperforms the state-of-the-art.

A shape-changing table-top display with haptic feedback allows its users to perceive 3D visual and texture displays interactively. Since few existing devices are developed as accurate displays with regulatory haptic feedback, a novel attentive and immersive shape changing mechanical interface (SCMI) consisting of image processing unit and transformation unit was proposed in this paper. In order to support a precise 3D table-top display with an offset of less than 2 mm, a custommade mechanism was developed to form precise surface and regulate the feedback force. The proposed image processing unit was capable of extracting texture data from 2D picture for rendering shape-changing surface and realizing 3D modeling. The preliminary evaluation result proved the feasibility of the proposed system.

An effective method is proved based on the scattering parameter of high speed 8-core coaxial-cable measured by vector network analyzer, and the semi-physical simulation is made to receive the eye diagram at different data transmission rate. The result can be apply to analysis decay and distortion of the signal through the coaxial-cable at high frequency, and can extensively design for electromagnetic compatibility of high-speed data transmission system.

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling，which can improve efficiency. A method of two-dimensional principal component analysis（2DPCA） is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

In a low-light scene, capturing color images needs to be at a high-gain setting or a long-exposure setting to avoid a visible flash. However, such these setting will lead to color images with serious noise or motion blur. Several methods have been proposed to improve a noise-color image through an invisible near infrared flash image. A novel method is that the luminance component and the chroma component of the improved color image are estimated from different image sources [1]. The luminance component is estimated mainly from the NIR image via a spectral estimation, and the chroma component is estimated from the noise-color image by denoising. However, it is challenging to estimate the luminance component. This novel method to estimate the luminance component needs to generate the learning data pairs, and the processes and algorithm are complex. It is difficult to achieve practical application. In order to reduce the complexity of the luminance estimation, an improved luminance estimation algorithm is presented in this paper, which is to weight the NIR image and the denoised-color image and the weighted coefficients are based on the mean value and standard deviation of both images. Experimental results show that the same fusion effect at aspect of color fidelity and texture quality is achieved, compared the proposed method with the novel method, however, the algorithm is more simple and practical.

Signal-to-noise ratio and MTF are important indexs to evaluate the performance of optical systems. However,whether they are used alone or joint assessment cannot intuitively describe the overall performance of the system. Therefore, an index is proposed to reflect the comprehensive system performance--Minimum Resolvable Radiation Performance Contrast (MRP) model. MRP is an evaluation model without human eyes. It starts from the radiance of the target and the background, transforms the target and background into the equivalent strips,and considers attenuation of the atmosphere, the optical imaging system, and the detector. Combining with the signal-to-noise ratio and the MTF, the Minimum Resolvable Radiation Performance Contrast is obtained. Finally the detection probability model of MRP is given.

To solve the problem that the remote sensing image segmentation speed is slow and the real-time performance is poor, this paper studies the method of remote sensing image segmentation based on Hadoop platform. On the basis of analyzing the structural characteristics of Hadoop cloud platform and its component MapReduce programming, this paper proposes a method of image segmentation based on the combination of OpenCV and Hadoop cloud platform. Firstly, the MapReduce image processing model of Hadoop cloud platform is designed, the input and output of image are customized and the segmentation method of the data file is rewritten. Then the Mean Shift image segmentation algorithm is implemented. Finally, this paper makes a segmentation experiment on remote sensing image, and uses MATLAB to realize the Mean Shift image segmentation algorithm to compare the same image segmentation experiment. The experimental results show that under the premise of ensuring good effect, the segmentation rate of remote sensing image segmentation based on Hadoop cloud Platform has been greatly improved compared with the single MATLAB image segmentation, and there is a great improvement in the effectiveness of image segmentation.

It’s not easy to discriminate dried wild-growing Lentinus edodes (WL) and cultivated Lentinus edodes (CL) by conventional method based on the morphological inspection of fruiting bodies. In this paper, fruiting body samples of WL and CL are discriminated by a tri-step IR spectroscopy method, including Fourier transform infrared (FT-IR) spectroscopy, second derivatives infrared (SD-IR) spectroscopy and two-dimensional correlation infrared (2D-IR) spectroscopy under thermal perturbation. The results show that the FT-IR spectra of WL and CL are similar in holistic spectral profile. More significant differences are exhibited in their SD-IR spectra in the range of 1700 - 900 cm^-1. Furthermore, more evident differences have been observed in their synchronous 2D-IR spectra in the range of 2970 - 2900, 1678 - 1390, 1250 -1104 and 1090 - 1030 cm^-1. The CL has thirteen auto-peaks at 2958, 2921, 1649, 1563, 1450, 1218, 1192, 1161, 1140, 1110, 1082, 1065 and 1047 cm^-1, in which the four strongest auto-peaks are at 2921, 1563, 1192 and 1082 cm^-1. The WL shows fifteen auto-peaks at 2960, 2937, 2921, 1650, 1615, 1555, 1458, 1219, 1190, 1138, 1111, 1084, 1068, 1048 and 1033 cm^-1, in which the four strongest auto-peaks are at 2921, 1650, 1190 and 1068 cm^-1. This study shows the potential of FT-IR spectroscopy and 2D correlation analysis in a simple and quick distinction of wild-growing and cultivated mushrooms.

Optical synthetic aperture imaging technology is an effective approach to improve imaging resolution. Compared with monolithic mirror system, the image of optical synthetic aperture system is often more complex at the edge, and as a result of the existence of gap between segments, which makes stitching becomes a difficult problem. So it is necessary to extract the edge of subaperture image for achieving effective stitching. Fractal dimension as a measure feature can describe image surface texture characteristics, which provides a new approach for edge detection. In our research, an improved differential box-counting method is used to calculate fractal dimension of image, then the obtained fractal dimension is mapped to grayscale image to detect edges. Compared with original differential box-counting method, this method has two improvements as follows: by modifying the box-counting mechanism, a box with a fixed height is replaced by a box with adaptive height, which solves the problem of over-counting the number of boxes covering image intensity surface; an image reconstruction method based on super-resolution convolutional neural network is used to enlarge small size image, which can solve the problem that fractal dimension can’t be calculated accurately under the small size image, and this method may well maintain scale invariability of fractal dimension. The experimental results show that the proposed algorithm can effectively eliminate noise and has a lower false detection rate compared with the traditional edge detection algorithms. In addition, this algorithm can maintain the integrity and continuity of image edge in the case of retaining important edge information.

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR’s analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR’s result better than PLSR.

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What’s more, the results inspire us the application of deep learning on SAR target posture description.

The absorption spectra of two kinds of important functional oligosaccharides were firstly acquired based on Fourier transform infrared spectroscopy in the range of 0.15-10THz. The simulation results of their infrared spectra were given based on Gaussian software, which were in good agreement with the experiment results. The rotation spectra and some perssad vibration spectra of these molecules were analyzed, and their absorption peaks were exactly identified. The components information was obtained by comparing the simulation results of different molecules.

A method of keyhole imaging based on camera array is realized to obtain the video image behind a keyhole in shielded space at a relatively long distance. We get the multi-angle video images by using a 2×2 CCD camera array to take the images behind the keyhole in four directions. The multi-angle video images are saved in the form of frame sequences. This paper presents a method of video frame alignment. In order to remove the non-target area outside the aperture, we use the canny operator and morphological method to realize the edge detection of images and fill the images. The image stitching of four images is accomplished on the basis of the image stitching algorithm of two images. In the image stitching algorithm of two images, the SIFT method is adopted to accomplish the initial matching of images, and then the RANSAC algorithm is applied to eliminate the wrong matching points and to obtain a homography matrix. A method of optimizing transformation matrix is proposed in this paper. Finally, the video image with larger field of view behind the keyhole can be synthesized with image frame sequence in which every single frame is stitched. The results show that the screen of the video is clear and natural, the brightness transition is smooth. There is no obvious artificial stitching marks in the video, and it can be applied in different engineering environment .

In order to solve the target fast tracking problem in embedded system, a moving target detecting and tracking algorithm based on a combination of three-frame difference and template matching is proposed. The system utilizes DSP to design a set of image processing equipment and DSP uses TI company’s DM6437.Three-frame difference can detect a initial position of the target, then Mean Normalized Product Correlation(NNPROD) template matching algorithm was utilized in a partial area to achieve a precise position and reduce the amount of calculation. The algorithm utilized four templates and image compression to fit pose and scale changes when moving. To meet the real-time requirement, an improved algorithm of NNPROD was proposed under certain lighting conditions, what ’ s more the C language code was optimized and TI company’s highly optimized VLIB vision library was reasonably utilized. After several tests, the results showed that NNPROD can fit the changing of environmental light well, but more time was needed. The improved method can still work well with the changes of pose and scale when the light changes less intensely , and the processing speed of the improved method increased from the previous 11F / s to 23F / s.

FOV separation (between VNIR sensor and SWIR sensor) and motion compensation imaging modes are introduced into the pushbroom imaging spectrometer to increase the SNR of the imaging data sometimes. Besides the higher SNR, the two imaging modes result in some bad effects on the imaging data, such as the additional misregistration. In the paper, a digital simulator for pushbroom Offner hyperspectral imaging spectrometer is used to analyze the misregistration caused by the FOV separation and the motion compensation imaging modes. Based on the imaging process, the simulator consists of a spatial response module, a spectral response module, and a radiometric response module. The FOV separation is simulated in the imaging position calculation process of the spatial response module, and the motion compensation is considered in both the imaging position simulation and the radiometric response module. Using the simulator, the imaging position data is created to quantify the misregistration. The result shows that the imaging track deviation, caused by the FOV separation, between the VNIR sensor and SWIR sensor keeps a constant quantity in the latitude direction. However, the deviation will increase along with the imaging time in the longitude direction. When the two imaging modes are both considered, the deviation is symmetrical relative to the nadir point in the latitude direction. However, the deviation is not symmetrical in the longitude. In order to analyze the misregistration effect on the imaging data, simulation data with different imaging modes on Dongtianshan remote sensing testing field is created using the simulator. And the misregistration effect on the spectra of flat ground pixel and rugged ground pixel are analyzed.

Fourier ptychography microscopy (FPM) provides gigapixel imaging with both a high image resolution and a wide field-of-view (FOV). However, it is time consuming during the image capture process. In this paper, we perform an analysis on the FPM imaging process. With numerical and experimental comparison, we find that the reconstructed high resolution images with half number of the total captured images is less degenerated compare to that using all the captured images, especially in the case that the object is amplitude or phase-only.

A detailed image processing of laser speckle interferometry is proposed as an example for the course of postgraduate student. Several image processing methods were used together for dealing with optoelectronic imaging system, such as the partial differential equations (PDEs) are used to reduce the effect of noise, the thresholding segmentation also based on heat equation with PDEs, the central line is extracted based on image skeleton, and the branch is removed automatically, the phase level is calculated by spline interpolation method, and the fringe phase can be unwrapped. Finally, the imaging processing method was used to automatically measure the bubble in rubber with negative pressure which could be used in the tire detection.

Three-dimensional imaging is increasingly becoming important in a number of applications that observe and analyze real-world environments. Range sensors, such as flash imaging Lidar and Time-of-flight camera, which can deliver high accuracy range measurement images, but are limited by the low resolution. To overcome this limitation, this paper shows the benefit of multimodal sensor system, combining a low-resolution range sensor with a high-resolution optical sensor, in order to provide a high-resolution, low-noise range image of the scene. First, an extrinsic calibration algorithm is used to align the range map with optical image. Then, an image-guided algorithm is proposed to solve the super-resolution optimization problem. This algorithm using the Markov Random Field framework. It defines an energy function that combines a standard quadratic data term and a regularizing term with the weighting factors that relate optical image edges to range map edges. Experiments on synthetic and real data are provided and analyzed to validate this method. The result confirms that the quality of the estimated high-resolution range map is improved. This work can be extended for video super-resolution with the consideration of temporal coherence.

In many situations, imagers are required to have higher imaging speed, such as gunpowder blasting analysis and observing high-speed biology phenomena. However, measuring high-speed video is a challenge to camera design, especially, in infrared spectrum. In this paper, we reconstruct a high-frame-rate video from compressive video measurements using temporal compressive imaging (TCI) with a temporal compression ratio T=8. This means that, 8 unique high-speed temporal frames will be obtained from a single compressive frame using a reconstruction algorithm. Equivalently, the video frame rates is increased by 8 times. Two methods, two-step iterative shrinkage/threshold (TwIST) algorithm and the Gaussian mixture model (GMM) method, are used for reconstruction. To reduce reconstruction time and memory usage, each frame of size 256×256 is divided into patches of size 8×8. The influence of different coded mask to reconstruction is discussed. The reconstruction qualities using TwIST and GMM are also compared.

Imaging Photoplethysmography (IPPG) is an emerging technique for the extraction of vital signs of human being using video recordings. IPPG technology with its advantages like non-contact measurement, low cost and easy operation has become one research hot spot in the field of biomedicine. However, the noise disturbance caused by non-microarterial area cannot be removed because of the uneven distribution of micro-arterial, different signal strength of each region, which results in a low signal noise ratio of IPPG signals and low accuracy of heart rate.

In this paper, we propose a method of improving the signal noise ratio of camera-based IPPG signals of each sub-region of the face using a weighted average. Firstly, we obtain the region of interest (ROI) of a subject’s face based camera. Secondly, each region of interest is tracked and feature-based matched in each frame of the video. Each tracked region of face is divided into 60x60 pixel block. Thirdly, the weights of PPG signal of each sub-region are calculated, based on the signal-to-noise ratio of each sub-region. Finally, we combine the IPPG signal from all the tracked ROI using weighted average. Compared with the existing approaches, the result shows that the proposed method takes modest but significant effects on improvement of signal noise ratio of camera-based PPG estimated and accuracy of heart rate measurement.

Hyperspectral imaging, recording intrinsic spectral information of the skin cross different spectral bands, become an important issue for robust face recognition. However, the main challenges for hyperspectral face recognition are high data dimensionality, low signal to noise ratio and inter band misalignment. In this paper, hyperspectral face recognition based on LBP (Local binary pattern) and SWLD (Simplified Weber local descriptor) is proposed to extract discriminative local features from spatio-spectral fusion information. Firstly, the spatio-spectral fusion strategy based on statistical information is used to attain discriminative features of hyperspectral face images. Secondly, LBP is applied to extract the orientation of the fusion face edges. Thirdly, SWLD is proposed to encode the intensity information in hyperspectral images. Finally, we adopt a symmetric Kullback-Leibler distance to compute the encoded face images. The hyperspectral face recognition is tested on Hong Kong Polytechnic University Hyperspectral Face database (PolyUHSFD). Experimental results show that the proposed method has higher recognition rate (92.8%) than the state of the art hyperspectral face recognition algorithms.

Computational ghost imaging(CGI), utilizing a single-pixel detector, has been extensively used in many fields. However, in order to achieve a high-quality reconstructed image, a large number of iterations are needed, which limits the flexibility of using CGI in practical situations, especially in the field of object recognition. In this paper, we purpose a method utilizing the feature matching to identify the number objects. In the given system, approximately 90% of accuracy of recognition rates can be achieved, which provides a new idea for the application of single pixel imaging in the field of object recognition

New virtual view is synthesized through depth image based rendering(DIBR) using a single color image and its associated depth map in 3D view generation. Holes are unavoidably generated in the 2D to 3D conversion process. We propose a hole-filling method based on depth map to address the problem. Firstly, we improve the process of DIBR by proposing a one-to-four (OTF) algorithm. The “z-buffer” algorithm is used to solve overlap problem. Then, based on the classical patch-based algorithm of Criminisi et al., we propose a hole-filling algorithm using the information of depth map to handle the image after DIBR. In order to improve the accuracy of the virtual image, inpainting starts from the background side. In the calculation of the priority, in addition to the confidence term and the data term, we add the depth term. In the search for the most similar patch in the source region, we define the depth similarity to improve the accuracy of searching. Experimental results show that the proposed method can effectively improve the quality of the 3D virtual view subjectively and objectively.

Mueller matrix is an optical parameter invasively to reveal the structure information of anisotropic material. Dental tissue has the ordered structure including dental enamel prism and dentinal tubule. The ordered structure of teeth surface will be destroyed by demineralization. The structure information has the possibility to reflect the dental demineralization. In the paper, the experiment setup was built to obtain the Mueller matrix images based on the dual- wave plate rotation method. Two linear polarizer and two quarter-wave plate were rotated by electric control revolving stage respectively to capture 16 images at different group of polarization states. Therefore, Mueller matrix image can be calculated from the 16 images. On this basis, depolarization index, the diattenuation index and retardance index of the Mueller matrix were analyzed by Lu-Chipman polarization decomposition method. Mueller matrix images of artificial demineralized enamels at different stages were analyzed and the results show the possibility to detect the dental demineralization using Mueller matrix imaging method.

Depth measurement is the most basic measurement in various machine vision, such as automatic driving, unmanned aerial vehicle (UAV), robot and so on. And it has a wide range of use. With the development of image processing technology and the improvement of hardware miniaturization and processing speed, real-time depth measurement using dual cameras has become a reality. In this paper, an embedded AM5728 and the ordinary low-cost dual camera is used as the hardware platform. The related algorithms of dual camera calibration, image matching and depth calculation have been studied and implemented on the hardware platform, and hardware design and the rationality of the related algorithms of the system are tested. The experimental results show that the system can realize simultaneous acquisition of binocular images, switching of left and right video sources, display of depth image and depth range. For images with a resolution of 640 × 480, the processing speed of the system can be up to 25 fps. The experimental results show that the optimal measurement range of the system is from 0.5 to 1.5 meter, and the relative error of the distance measurement is less than 5%. Compared with the PC, ARM11 and DMCU hardware platforms, the embedded AM5728 hardware is good at meeting real-time depth measurement requirements in ensuring the image resolution.

In this paper, a new BCE with multi-focusing microlens array (MLA) was proposed. The BCE consist of detachable micro-hole array (MHA), multi-focusing MLA and spherical substrate, thus allowing it to have a large FOV without crosstalk and stray light. The MHA was fabricated by the precision machining and the parameters of the microlens varied depend on the aperture of micro-hole, through which the implementation of the multi-focusing MLA was realized under the negative pressure. Without the pattern transfer and substrate reshaping, the whole fabrication method was capable of accomplishing within several minutes by using microinjection technology. Furthermore, the method is cost-effective and easy for operation, thus providing a feasible method for the mass production of the BCE. The corresponding image processing was used to realize the image stitching for the sub-image of each single microlens, which offering an integral image in large FOV. The image stitching was implemented through the overlap between the adjacent sub-images and the feature points between the adjacent sub-images were captured by the Harris point detection. By using the adaptive non-maximal suppression, numerous potential mismatching points were eliminated and the algorithm efficiency was proved effectively. Following this, the random sample consensus (RANSAC) was used for feature points matching, by which the relation of projection transformation of the image is obtained. The implementation of the accurate image matching was then realized after the smooth transition by weighted average method. Experimental results indicate that the image-stitching algorithm can be applied for the curved BCE in large field.

Binocular vision imaging system, which has a small field of view, cannot reconstruct the 3-D shape of the dynamic object.

We found a linear array CCD binocular vision imaging system, which uses different calibration and reconstruct methods. On the basis of the binocular vision imaging system, the linear array CCD binocular vision imaging systems which has a wider field of view can reconstruct the 3-D morphology of objects in continuous motion, and the results are accurate. This research mainly introduces the composition and principle of linear array CCD binocular vision imaging system, including the calibration, capture, matching and reconstruction of the imaging system. The system consists of two linear array cameras which were placed in special arrangements and a horizontal moving platform that can pick up objects. The internal and external parameters of the camera are obtained by calibrating in advance. And then using the camera to capture images of moving objects, the results are then matched and 3-D reconstructed.

The linear array CCD binocular vision imaging systems can accurately measure the 3-D appearance of moving objects, this essay is of great significance to measure the 3-D morphology of moving objects.

Binocular stereo vision is becoming a research hotspot in the area of image processing. Based on traditional adaptive-weight stereo matching algorithm, we improve the cost volume by averaging the AD (Absolute Difference) of RGB color channels and adding x-derivative of the grayscale image to get the cost volume. Then we use guided filter in the cost aggregation step and weighted median filter for post-processing to address the edge problem. In order to get the location in real space, we combine the deep information with the camera calibration to project each pixel in 2D image to 3D coordinate matrix. We add the concept of projection to region-growing algorithm for surface reconstruction, its specific operation is to project all the points to a 2D plane through the normals of clouds and return the results back to 3D space according to these connection relationship among the points in 2D plane. During the triangulation in 2D plane, we use Delaunay algorithm because it has optimal quality of mesh. We configure OpenCV and pcl on Visual Studio for testing, and the experimental results show that the proposed algorithm have higher computational accuracy of disparity and can realize the details of the real mesh model.

Hyperspectral light field (HSLF) images with enriched spectral and angular information provide better representation of real scenes than conventional 2D images. In this paper, we propose a novel denoising method for HSLF images. The proposed method consists of two main steps. First, we generalize the intrinsic tensor sparsity (ITS) measure previously used for 3D hyperspectral image denoising to the 5D HSLF, by using the global correlation along the spectral dimension and the nonlocal similarity across the spatial and angular dimensions. Second, we further exploit the spatial-angular correlation by integrating light field super-resolution (SR) into the denoising process. In this way, the 5D HSLF can be better recovered. Experimental results validate the superior performance of the proposed method in terms of both objective and subjective quality on a self-collected HSLF dataset, in comparison with directly applying the state-of-the-art denoising methods.

In this paper, we propose a two-dimensional (2-D) triangular lattice photonic crystal plate by close-packed SiO2/ TiO2 layers with the stacking mode of ABABABA. By using the finite-difference time-domain (FDTD) method, negative refraction of a single Gaussian beam incident plate with different angles are respectively demonstrated; clear image spots of a point source with normalized frequency ω=0.3605(2πc/a) vertical incident media plate are obtained in the image plane. It can be found that the imaging properties are as same as the isotropic homogeneous medium with refractive index n=-1. The measurement results show that when the distance between the image and the upper surface of the sample V is 5.12a, 3.09a and 1.15a, the distance between the source and the lower surface of the sample U is a, 3a and 5a, respectively. This means that the sum of U and V is mostly equal to the thickness of the plate L and the negative effect of near-perfect lens is realized. This proposed structure with negative refraction properties may have great applications for the design of photonic crystal focusing devices.

A natural-color mapping for single-band night-time image method based on FPGA can transmit the color of the reference image to single-band night-time image, which is consistent with human visual habits and can help observers identify the target. This paper introduces the processing of the natural-color mapping algorithm based on FPGA. Firstly, the image can be transformed based on histogram equalization, and the intensity features and standard deviation features of reference image are stored in SRAM. Then, the real-time digital images’ intensity features and standard deviation features are calculated by FPGA. At last, FPGA completes the color mapping through matching pixels between images using the features in luminance channel.

Confocal Raman Microscopy (CRM) has matured to become one of the most powerful instruments in analytical science because of its molecular sensitivity and high spatial resolution. Compared with conventional Raman Microscopy, CRM can perform three dimensions mapping of tiny samples and has the advantage of high spatial resolution thanking to the unique pinhole. With the wide application of the instrument, there is a growing requirement for the evaluation of the imaging performance of the system. Point-spread function (PSF) is an important approach to the evaluation of imaging capability of an optical instrument. Among a variety of measurement methods of PSF, the point source method has been widely used because it is easy to operate and the measurement results are approximate to the true PSF. In the point source method, the point source size has a significant impact on the final measurement accuracy. In this paper, the influence of the point source sizes on the measurement accuracy of PSF is analyzed and verified experimentally. A theoretical model of the lateral PSF for CRM is established and the effect of point source size on full-width at half maximum of lateral PSF is simulated. For long-term preservation and measurement convenience, PSF measurement phantom using polydimethylsiloxane resin, doped with different sizes of polystyrene microspheres is designed. The PSF of CRM with different sizes of microspheres are measured and the results are compared with the simulation results. The results provide a guide for measuring the PSF of the CRM.

In contrast to the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system doesn’t need a WFS to measure the wavefront aberrations. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. The model-based WFSless system has a great potential in real-time correction applications because of its fast convergence. The control algorithm of the model-based WFSless system is based on an important theory result that is the linear relation between the Mean-Square Gradient (MSG) magnitude of the wavefront aberration and the second moment of the masked intensity distribution in the focal plane (also called as Masked Detector Signal-MDS). The linear dependence between MSG and MDS for the point source imaging with a CCD sensor will be discussed from theory and simulation in this paper. The theory relationship between MSG and MDS is given based on our previous work. To verify the linear relation for the point source, we set up an imaging model under atmospheric turbulence. Additionally, the value of MDS will be deviate from that of theory because of the noise of detector and further the deviation will affect the correction effect. The theory results under noise will be obtained through theoretical derivation and then the linear relation between MDS and MDS under noise will be discussed through the imaging model. Results show the linear relation between MDS and MDS under noise is also maintained well, which provides a theoretical support to applications of the model-based WFSless system.

In this paper, we proposed a method of canopy reconstruction and measurement based on 3D super resolution range-gated imaging. In this method, high resolution 2D intensity images are grasped by active gate imaging, and 3D images of canopy are reconstructed by triangular-range-intensity correlation algorithm at the same time. A range-gated laser imaging system(RGLIS) is established based on 808 nm diode laser and gated intensified charge-coupled device (ICCD) camera with 1392´1040 pixels. The proof experiments have been performed for potted plants located 75m away and trees located 165m away. The experiments show it that can acquire more than 1 million points per frame, and 3D imaging has the spatial resolution about 0.3mm at the distance of 75m and the distance accuracy about 10 cm. This research is beneficial for high speed acquisition of canopy structure and non-destructive canopy measurement.

As an important pollution source, the noise pollution is always the researcher’s focus. Especially in recent years, the noise pollution is seriously harmful to the human beings’ environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

In this paper, we reported the laboratory spectral calibration of an ultraviolet (UV) Fourier transform imaging spectrometer (FTIS). A short overview of the designed UV-FTIS, which feature with a Cassegrain objective, an Offner relay optics system and a spatial-and-temporal modulation Michelson structure, is given. The experimental setup of spectral calibration is described, including details of the light source and integrating sphere. A high pressure mercury lamp was used to acquire reference spectrum. We calculated the all optical path difference (OPD) to achieve spectral response of every wavelength sample and divided the position of reference peak to subpixel to increase the precision of spectral calibration. The spectrum of spectral calibration show two weakly responded peaks, which was validated by reference spectrum of fiber optic spectrometer. The deviation of wavelength calibration is low to establish a best spectrometer resolution. The results of spectral calibration can meet the requirements of the UV-FTIS application.

Moving target detection is important for the application of target tracking and remote surveillance in active range-gated laser imaging. This technique has two operation modes based on the difference of the number of pulses per frame: stroboscopic mode with the accumulation of multiple laser pulses per frame and flash mode with a single shot of laser pulse per frame. In this paper, we have established a range-gated laser imaging system. In the system, two types of lasers with different frequency were chosen for the two modes. Electric fan and horizontal sliding track were selected as the moving targets to compare the moving blurring between two modes. Consequently, the system working in flash mode shows more excellent performance in motion blurring against stroboscopic mode. Furthermore, based on experiments and theoretical analysis, we presented the higher signal-to-noise ratio of image acquired by stroboscopic mode than flash mode in indoor and underwater environment.

Fourier ptychography is a new type of synthetic aperture imaging technique based on phase retrieval method which can improve microscopeic imaging performance beyond the diffraction limit of the employed optical components by illuminating the object with oblique waves of different incident angles where the field of view remains unchanged. illumination angle and the overlap rate of spectrum will have a certain impact on the quality of reconstruction. In this paper, we study the effects of illumination angle and spectral overlap rate on the image quality of Fourier ptychography. The simulation results show that increasing the illumination angle and spectral overlap can improve the resolution, but there is a threshold for the key parameters of spectral overlap rate. The convergence rate decreases when the overlap rate exceeds 70%, and the reconstruction process is more time-consuming due to the high overlap rate. However the results of proposed study shows that an overlap of 60% is the optimal choice to acquire a high-quality recovery with high speed.

In this paper, a motion deblurring method with long/short exposure image pairs is presented. The long/short exposure image pairs are captured for the same scene under different exposure time. The image pairs are treated as the input of the deblurring method and more information could be used to obtain a deblurring result with high image quality. Firstly, the luminance equalization process is carried out to the short exposure image. And the blur kernel is estimated with the image pair under the maximum a posteriori (MAP) framework using conjugate gradient algorithm. Then a L0 image smoothing based denoising method is applied to the luminance equalized image. And the final deblurring result is obtained with the gain controlled residual image deconvolution process with the edge map as the gain map. Furthermore, a real experimental optical system is built to capture the image pair in order to demonstrate the effectiveness of the proposed deblurring framework. The long/short image pairs are obtained under different exposure time and camera gain control. Experimental results show that the proposed method could provide a superior deblurring result in both subjective and objective assessment compared with other deblurring approaches.

Eye tracker is an important instrument for research in psychology, widely used in attention, visual perception, reading and other fields of research. Because of its potential function in human-computer interaction, the eye gaze tracking has already been a topic of research in many fields over the last decades. Nowadays, with the development of technology, non-intrusive methods are more and more welcomed. In this paper, we will present a method based on the shape of pupil image to estimate the gaze point of human eyes without any other intrusive devices such as a hat, a pair of glasses and so on. After using the ellipse fitting algorithm to deal with the pupil image we get, we can determine the direction of the fixation by the shape of the pupil.The innovative aspect of this method is to utilize the new idea of the shape of the pupil so that we can avoid much complicated algorithm. The performance proposed is very helpful for the study of eye gaze tracking, which just needs one camera without infrared light to know the changes in the shape of the pupil to determine the direction of the eye gazing, no additional condition is required.

Compressed Sensing (CS) can use the sparseness of a target to obtain its image with much less data than that defined by the Nyquist sampling theorem. In this paper, we study the hardware implementation of a block compression sensing system and its reconstruction algorithms. Different block sizes are used. Two algorithms, the orthogonal matching algorithm (OMP) and the full variation minimum algorithm (TV) are used to obtain good reconstructions. The influence of block size on reconstruction is also discussed.

Human eyes are never completely static even when they are fixing a stationary point. These irregular, small movements, which consist of micro-tremors, micro-saccades and drifts, can prevent the fading of the images that enter our eyes. The importance of researching the fixational eye movements has been experimentally demonstrated recently. However, the characteristics of fixational eye movements and their roles in visual process have not been explained clearly, because these signals can hardly be completely extracted by now. In this paper, we developed a new eye movement detection device with a high-speed camera. This device includes a beam splitter mirror, an infrared light source and a high-speed digital video camera with a frame rate of 200Hz. To avoid the influence of head shaking, we made the device wearable by fixing the camera on a safety helmet. Using this device, the experiments of pupil tracking were conducted. By localizing the pupil center and spectrum analysis, the envelope frequency spectrum of micro-saccades, micro-tremors and drifts are shown obviously. The experimental results show that the device is feasible and effective, so that the device can be applied in further characteristic analysis.

Optical Diffraction Tomography (ODT), as a novel 3D imaging technique, can obtain a 3D refractive index (RI) distribution to reveal the important optical properties of transparent samples. According to the theory of ODT, an optical diffraction tomography setup is built based on the Mach-Zehnder interferometer. The propagation direction of object beam is controlled by a 2D translation stage, and 121 holograms based on different illumination angles are recorded by a Charge-coupled Device (CCD). In order to prove the validity and accuracy of the ODT, the 3D RI profile of microsphere with a known RI is firstly measured. An iterative constraint algorithm is employed to improve the imaging accuracy effectively. The 3D morphology and average RI of the microsphere are consistent with that of the actual situation, and the RI error is less than 0.0033. Then, an optical element fabricated by laser with a non-uniform RI is taken as the sample. Its 3D RI profile is obtained by the optical diffraction tomography system.

The infrared detection technology of ballistic missile based on near space platform can effectively make up the shortcomings of high-cost of traditional early warning satellites and the limited earth curvature of ground-based early warning radar. In terms of target detection capability, aiming at the problem that the formula of the action distance based on contrast performance ignores the background emissivity in the calculation process and the formula is only valid for the monochromatic light, an improved formula of the detecting range based on contrast performance is proposed. The near space infrared imaging system parameters are introduced, the expression of the contrastive action distance formula based on the target detection of the near space platform is deduced. The detection range of the near space infrared system for the booster stage ballistic missile skin, the tail nozzle and the tail flame is calculated. The simulation results show that the near-space infrared system has the best effect on the detection of tail-flame radiation.

In the field of remote sensing image processing, remote sensing image segmentation is a preliminary step for later analysis of remote sensing image processing and semi-auto human interpretation, fully-automatic machine recognition and learning. Since 2000, a technique of object-oriented remote sensing image processing method and its basic thought prevails. The core of the approach is Fractal Net Evolution Approach (FNEA) multi-scale segmentation algorithm. The paper is intent on the research and improvement of the algorithm, which analyzes present segmentation algorithms and selects optimum watershed algorithm as an initialization. Meanwhile, the algorithm is modified by modifying an area parameter, and then combining area parameter with a heterogeneous parameter further. After that, several experiments is carried on to prove the modified FNEA algorithm, compared with traditional pixel-based method (FCM algorithm based on neighborhood information) and combination of FNEA and watershed, has a better segmentation result.