Spectral responsivity calibrations of two different types of pyroelectric radiometers have been made in the infrared region up to 14 μm in power mode using three different calibration facilities at NIST. One pyroelectric radiometer is a temperature-controlled low noise-equivalent-power (NEP) single-element pyroelectric radiometer with an active area of 5 mm in diameter. The other radiometer is a prototype using the same type of pyroeletric detector with dome-input optics, which was designed to increase absorptance and to minimize spectral structures to obtain a constant spectral responsivity. Three calibration facilities at NIST were used to conduct direct and indirect responsivity calibrations tied to absolute scales in the infrared spectral regime. We report the calibration results for the single-element pyroelectric radiometer using a new Infrared Spectral Comparator Facility (IRSCF) for direct calibration. Also, a combined method using the Fourier Transform Infrared Spectrophotometry (FTIS) facility and single wavelength laser tie-points are described to calibrated standard detectors with an indirect approach. For the dome-input pyroelectric radiometer, the results obtained from another direct calibration method using a circular variable filter (CVF) spectrometer and the FTIS are also presented. The inter-comparison of different calibration methods enables us to improve the responsivity uncertainty performed by the different facilities. For both radiometers, consistent results of the spectral power responsivity have been obtained applying different methods from 1.5 μm to 14 μm with responsivity uncertainties between 1 % and 2 % (k = 2). Relevant characterization results, such as spatial uniformity, linearity, and angular dependence of responsivity, are shown. Validation of the spectral responsivity calibrations, uncertainty sources, and improvements for each method will also be discussed.

The GStreamer architecture allows for simple modularized processing. Individual GStreamer elements have been developed that allow for control, measurement, and ramping of a blackbody, for capturing continuous imagery from a sensor, for segmenting out a MRTD target, for applying a blur equivalent to that of a human eye and a display, and for thresholding a processed target contrast for "calling" it. A discussion of each of the components will be followed by an analysis of its performance relative to that of human observers.

Sensitive infrared working-standard detectors with large active area are needed to extend the signal dynamic range of the National Institute of Standards and Technology (NIST) pyroelectric transfer-standards used for infrared spectral power responsivity calibrations. Increased sensitivity is especially important for irradiance mode responsivity measurements. The noise equivalent power (NEP) of the NIST used pyroelectric transfer-standards is about 8 nW/Hz^1/2, equal to a D*= 5.5 x 10⁷ cm Hz^1/2/W. A large-area photovoltaic HgCdTe (PV-MCT) detector was custom made for the 2.5 μm to 11 μm wavelength range using a 4-stage thermoelectric cooler. At least an order of magnitude lower NEP was expected than that of the pyroelectric transfer-standards to measure irradiance. The large detector area was produced with multiple p-n junctions. The periodical, multiple-junction structure produced a spatial non-uniformity in the detector response. The PV-MCT radiometer was characterized for spatial non-uniformity of response using different incident beam sizes to evaluate the uncertainty component caused by the spatial non-uniformity. The output voltage noise and also the current and voltage responsivities were evaluated at different signal gains and frequencies. The output voltage noise was decreased and the voltage responsivity was increased to lower the NEP of the radiometer. The uncertainty of the spectral power responsivity measurements was evaluated. It is recommended to use a bootstrap type trans-impedance amplifier along with a cold field-of-view limiter to improve the NEP of the PV-MCT radiometer.

The modulation transfer function (MTF) measurement is critical for understanding the performance of an EOIR system. Unfortunately, due to both spatially correlated and spatially un-correlated noise sources, the performance of the MTF measurement (specifically near the cutoff) can be severely degraded. When using a 2D imaging system, the intrinsic sampling of the 1D edge spread function (ESF) allows for redundant samples to be averaged suppressing the noise contributions. The increase in the signal-to-noise will depend on the angle of the edge with respect to the sampling along with the specified re-sampling rate. In this paper, we demonstrate how the information in the final ESF can be used to identify the contribution of noise. With an estimate of the noise, the noise-limited portion of MTF measurement can be identified. Also, we demonstrate how the noise-limited portion of the MTF measurement can be used in combination with a fitting routine to provide a smoothed measurement.

Using band-limited LASER speckle to measure the Modulation Transfer Function (MTF) of an image sensor offers simplified procedure and inexpensive laboratory set up compared with the traditional method of using a knife edge on the sensor imaging plane. This speckle technique has been previously demonstrated by Glen Boreman's group on devices in the visible range. We have extended the procedure to short-wave infrared (IR) sensor at 1.55 micron. Similar measurements were also made at 532 nanometer on a commercial visible (VIS) sensor. The experiments show that the LASER speckle method to be accurate when compared to knife-edge measurements for data below Nyquist. The measured MTF data support optical system design and image quality modeling for both VIS and IR sensing applications.

While it is now common practice to use a trend removal to eliminate low frequency xed pattern noise in thermal imaging systems, there is still some disagreement as to whether one means of trend removal is better than another and whether or not the strength of the trend removal should be limited. The dierent methods for trend removal will be presented as well as an analysis of the calculated noise as a function of their strengths will be presented for various thermal imaging systems. In addition, trend removals were originally put in place in order to suppress the low-frequency component of the Sigma VH term. It is now prudent to perform a trend removal at an intermediate noise calculation step in order to suppress the low frequency component of both the Sigma V and Sigma H components. A discussion of the ramications of this change in measurement will be included for thermal modeling considerations.

The Quadri-Wave Lateral Shearing Interferometry (QWLSI) is an innovative wave front sensing technique that is commercially available for MWIR and LWIR applications. We present this technology and its application to the metrology, on and off-axis, of infrared imaging systems. The bench is only composed of a collimated reference beam that creates a source point at infinity, the objective to analyze and the sensor placed a few millimeters after the focal spot. Thanks to this direct measurement configuration, the alignment process is very simple and fast. A complete characterization (aberrations, MTF, field curvature) for several field points is possible within a few minutes.

Santa Barbara Infrared, Inc. has deployed IRWindows as an Electro-Optical test development and execution environment for military Test Program Sets (TPS). Advantages of TPS development for EO systems in the IRWindows environment are seen compared to the TPS development in ATLAS. The advantages of the IRWindows environment are: 1. Faster learning curve (graphical user interface is easier than test line interface) 2. Faster TPS development time (real time changes and asset control interface allows for faster development) 3. Asset control panel allows user to control assets real time and monitor all asset functions during development 4. Unit Under Test (UUT) image viewer allows user to set test parameters like Region of Interest more easily and more precisely 5. Continuous mode tests (like MTF allows user to real time adjustments) 6. Open architecture for test modifications This paper will outline the details of how these advantages are utilized and how not only development time is decreased but also how test execution time can be minimized making traditionally long TPS run times on EO systems more efficient.

Imaging systems have advanced significantly in the last decades in terms of low noise and better resolution. While imaging hardware resolution can be limited by collection aperture size or by the camera modulation transfer function (MTF), it is the atmosphere that usually limits image quality for long range imaging. The main atmospheric distortions are caused by optical turbulence, absorption, and scattering by particulates in the atmosphere. The effects of the turbulent medium over long/short exposures are image blur and wavefront tilts that cause spatio-temporal image shifts. This blur limits the frequency of line pairs that can be resolved in the target's image and thus affects the ability to acquire targets. The observer appears to be able to ignore large-scale distortions while small-scale distortions blur the image and degrade resolution. Resolution degradations due to turbulence are included in current performance models by the use of an atmospheric MTF. Turbulence distortion effects are characterized by both short and long exposure MTFs. In addition to turbulence, scattering and absorption produced by molecules and aerosols in the atmosphere cause both attenuation and additional image blur according to the atmospheric aerosol MTF. The absorption can have significant effect on target acquisition in infrared (IR) imaging. In the present work, a brief overview and discussion of atmospheric effects on target acquisition in the IR is given.

Artificial displacement (the apparent motion of stationary objects) is one important component of atmospheric turbulence distortion, which has led many researchers to propose motion compensation as a solution. Defining a sufficiently dense set of motion estimates for successful restoration is challenging, particularly for time sensitive applications. We introduce a new, control grid implementation of optical flow that allows for rapid and analytical solutions to the motion estimation problem. Our results demonstrate the effectiveness of using the resulting motion field for removing articial displacements in turbulence distorted videos.

Image quality in high altitude long range imaging systems can be severely limited by atmospheric absorption, scattering, and turbulence. Atmospheric aerosols contribute to this problem by scattering target signal out of the optical path and by scattering in unwanted light from the surroundings. Target signal scattering may also lead to image blurring though, in conventional modeling, this effect is ignored. The validity of this choice is tested in this paper by developing an aerosol modulation transfer function (MTF) model for an inhomogeneous atmosphere and then applying it to real-world scenarios using MODTRAN derived scattering parameters. The resulting calculations show that aerosol blurring can be effectively ignored.

In this paper we present a new approach to deblur the effect of atmospheric turbulence in the case of long range imaging. Our method is based on an analytical formulation, the Fried kernel, of the atmosphere modulation transfer function (MTF) and a framelet based deconvolution algorithm. An important parameter is the refractive index structure which requires specific measurements to be known. Then we propose a method which provides a good estimation of this parameter from the input blurred image. The final algorithms are very easy to implement and show very good results on both simulated blur and real images.

We recently developed a new approach to get a stabilized image from a sequence of frames acquired through atmospheric turbulence. The goal of this algorihtm is to remove the geometric distortions due by the atmosphere movements. This method is based on a variational formulation and is efficiently solved by the use of Bregman iterations and the operator splitting method. In this paper we propose to study the influence of the choice of the regularizing term in the model. Then we proposed to experiment some of the most used regularization constraints available in the litterature.

Many remote sensing applications are concerned with observing objects over long horizontal paths and often the atmosphere between observer and object is quite turbulent, especially in arid or semi-arid regions. Depending on the degree of turbulence, atmospheric turbulence can cause quite severe image degradation, the foremost effects being temporal and spatial blurring. And since the observed objects are not necessarily stationary, motion blurring can also factor in the degradation process. At present, the majority of these image processing methods aim exclusively at the restoration of static scenes. But there is a growing interest in enhancing turbulence mitigation methods to include moving objects as well. Therefore, the approach in this paper is to employ block-matching as motion detection algorithm to detect and estimate object motion in order to separate directed movement from turbulence-induced undirected motion. This enables a segmentation of static scene elements and moving objects, provided that the object movement exceeds the turbulence motion. Local image stacking is carried out for the moving elements, thus effectively reducing motion blur created by averaging and improving the overall final image restoration by means of blind deconvolution.

As is well known, the turbulent coherence diameter is evaluated via an integral over path varying turbulence. However, a recent analysis also suggests a system aperture size effect that interacts with the coherence diameter effect. This effect, due to the phase structure function, produces an altered behavior on the short-exposure atmospheric modulation transfer function (MTF). This behavior can be modeled as multiplicative adjustments to two dimensionless imaging scenario parameters. To illustrate these effects, path dependent turbulence effects are introduced through the context of a daytime convective boundary layer scenario featuring turbulence strength that varies as a function of height to the minus-four-thirds power. Two path geometry cases are studied: slant path propagation above flat terrain, where the object viewed and observer are at varying heights, and propagation between an object viewed and an observer at equal heights above the terrain situated on opposite sides of a valley. Results for both cases show the newly proposed atmospheric MTF is unaltered in form, but that path dependent scaling laws apply to the two governing dimensionless parameters. Scaling relations are plotted for each case studied, and the integral relations developed can be easily computed for further specific cases.

Turbulence mitigation techniques require input data representing a wide variety of turbulent atmospheric and weather conditions in order to produce robust results and wider ranges of applicability. In the past, this has implied the need for numerous data collection equipment items to account for multiple frequency bands and various system configurations. However, recent advancements in turbulence simulation techniques have resulted in viable options to real-time data collection with various levels of available simulation accuracy. This treatment will detail the development and implementation of an extension to the second order statistical turbulence simulation model presented by Repasi¹ and others. The Repasi model is extended to include the effects of various wavelengths, optical configurations, and short exposure imaging on angle of arrival fluctuation statistics. The result of the development is an atmospheric turbulence simulation technique that is physics-based but less computationally intensive than phase-based or deflector screen approaches. In these cases, the statistical approach detailed in this paper provides the user with an opportunity to obtain a better trade-off between accuracy and simulation run-time. The mathematical development and reasoning behind the changes to the previous statistical model will be presented, and sample imagery produced by the extended technique will be included. The result is a model that captures the major turbulence effects required for algorithm development for large classes of mitigation techniques.

Energy conservation is an essential feature of the optical waves propagating through refractive turbulence. It was well understood for almost 30 years, that energy conservation has a very important consequence for the fluctuations in the images of the incoherent objects observed through turbulence. Namely the image of the uniformly illuminated areas of the object does not scintillate. As a consequence the low-contrast parts of the scene exhibit weak fluctuation even for very strong turbulence, but scintillations near the sharp edges can be strong even for the weak turbulence. Energy conservation property of the turbulent Point Spread Function (PSF) is essential for modeling of the turbulent image distortions, both for the development of the image processing techniques and for simulations of the turbulent imaging. However it is completely neglected in the current literature on the turbulent imaging theory and modeling. We discuss the relations between the energy conservation and anisoplanatism for the most common turbulence imaging models. Our analysis reveals that the only isoplanatic authentic turbulent PSF that is compliant with energy conservation corresponds to the thin aperture plane phase screen model of turbulence. This implies that for the near-the-ground imaging, and even for the astronomical-type imaging under strong turbulence conditions the turbulent PSF has to be modeled as a random function of four arguments with certain constraints. We show some practical ways how the three functional constrains on the turbulent PSF: nonnegative values, finite bandwidth and energy conservation can be complied with in practical generation of turbulent PSF.

During the FATMOSE trial, held over the False Bay (South Africa) from November 2009 until October 2010, day and night (24/7) high resolution images were collected of point sources at a range of 15.7 km. Simultaneously, data were collected on atmospheric parameters, as relevant for the turbulence conditions: air- and sea temperature, windspeed, relative humidity and the structure parameter for refractive index: C_n ². The data provide statistical information on the mean value and the variance of the atmospheric point spread function and the associated modulation transfer function during series of consecutive frames. This information allows the prediction of the range performance for a given sensor, target and atmospheric condition, which is of great importance for the user of optical sensors in related operational areas and for the developers of image processing algorithms. In addition the occurrence of "lucky shots" in series of frames is investigated: occasional frames with locally small blur spots. The simultaneously measured short exposure blur and the beam wander are compared with simultaneously collected scintillation data along the same path and the C_n ² data from a locally installed scintillometer. By using two vertically separated sources, the correlation is determined between the beam wander in their images, providing information on the spatial extension of the atmospheric turbulence (eddy size). Examples are shown of the appearance of the blur spot, including skewness and astigmatism effects, which manifest themselves in the third moment of the spot and its distortion. An example is given of an experiment for determining the range performance for a given camera and a bar target on an outgoing boat in the False Bay.

A Forward Looking Interferometer (FLI) sensor has the potential to be used as a means of detecting aviation hazards in flight. One of these hazards is mountain wave turbulence. The results from a data acquisition activity at the University of Colorado's Mountain Research Station will be presented here. Hyperspectral datacubes from a Telops Hyper-Cam are being studied to determine if evidence of a turbulent event can be identified in the data. These data are then being compared with D&P TurboFT data, which are collected at a much higher time resolution and broader spectrum.

In the past five years, significant progress has been accomplished in the reduction of infrared detector pitch and detector size. Recently, longwave infrared detectors in limited quantities have been fabricated with a detector pitch of 5 micrometers. Detectors with 12 micrometer pitch are now becoming standard in both the midwave infrared (MWIR) and longwave infrared (LWIR) sensors. Persistent surveillance systems are pursuing 10 micrometer detector pitch in large format arrays. The fundamental question that most system designers and detector developers desire an answer to is: "how small can you produce an infrared detector and still provide value in performance?" If a system is mostly diffraction-limited, then developing a smaller detector is of limited benefit. If a detector is so small that it does not collect enough photons to produce a good image, then a smaller detector is not much benefit. Resolution and signal-tonoise are the primary characteristics of an imaging system that contribute to targeting, pilotage, search, and other human warfighting task performance. In this paper, we investigate the task of target discrimination range performance as a function of detector size/pitch. Results for LWIR and MWIR detectors are provided and depend on a large number of assumptions that are reasonable.

The use of the intensity change and line-of-sight (LOS) change concepts have previously been documented in the open-literature as techniques used by non-imaging infrared (IR) seekers to reject expendable IR countermeasures (IRCM). The purpose of this project was to implement IR counter-countermeasure (IRCCM) algorithms based on target intensity and kinematic behavior for a generic imaging IR (IIR) seeker model with the underlying goal of obtaining a better understanding of how expendable IRCM can be used to defeat the latest generation of seekers. The report describes the Intensity Ratio Change (IRC) and LOS Rate Change (LRC) discrimination techniques. The algorithms and the seeker model are implemented in a physics-based simulation product called Tactical Engagement Simulation Software (TESS^™). TESS is developed in the MATLAB®/Simulink® environment and is a suite of RF/IR missile software simulators used to evaluate and analyze the effectiveness of countermeasures against various classes of guided threats. The investigation evaluates the algorithm and tests their robustness by presenting the results of batch simulation runs of surface-to-air (SAM) and air-to-air (AAM) IIR missiles engaging a non-maneuvering target platform equipped with expendable IRCM as self-protection. The report discusses how varying critical parameters such track memory time, ratio thresholds and hold time can influence the outcome of an engagement.

In general, long range visual detection, recognition and identification are hampered by turbulence caused by atmospheric conditions. Much research has been devoted to the field of turbulence compensation. One of the main advantages of turbulence compensation is that it enables visual identification over larger distances. In many (military) scenarios this is of crucial importance. In this paper we give an overview of several software and hardware approaches to compensate for the visual artifacts caused by turbulence. These approaches are very diverse and range from the use of dedicated hardware, such as adaptive optics, to the use of software methods, such as deconvolution and lucky imaging. For each approach the pros and cons are given and it is indicated for which scenario this approach is useful. In more detail we describe the turbulence compensation methods TNO has developed in the last years and place them in the context of the different turbulence compensation approaches and TNO's turbulence compensation roadmap. Furthermore we look forward and indicate the upcoming challenges in the field of turbulence compensation.

Imagery acquired with modern imaging systems is susceptible to a variety of degradations, including blur from the point spread function (PSF) of the imaging system, aliasing from undersampling, blur and warping from atmospheric turbulence, and noise. A variety of image restoration methods have been proposed that estimate an improved image by processing a sequence of these degraded images. In particular, multi-frame image restoration has proven to be a particularly powerful tool for atmospheric turbulence mitigation (TM) and super-resolution (SR). However, these degradations are rarely addressed simultaneously using a common algorithm architecture, and few TM or SR solutions are capable of performing robustly in the presence of true scene motion, such as moving dismounts. Still fewer TM or SR algorithms have found their way into practical real-time implementations. In this paper, we describe a new L-3 joint TM and SR (TMSR) real-time processing solution and demonstrate its capabilities. The system employs a recently developed versatile multi-frame joint TMSR algorithm that has been implemented using a real-time, low-power FPGA processor system. The L-3 TMSR solution can accommodate a wide spectrum of atmospheric conditions and can robustly handle moving vehicles and dismounts. This novel approach unites previous work in TM and SR and also incorporates robust moving object detection. To demonstrate the capabilities of the TMSR solution, results using field test data captured under a variety of turbulence levels, optical configurations, and applications are presented. The performance of the hardware implementation is presented, and we identify specific insertion paths into tactical sensor systems.

Atmospheric turbulence can severely limit the range performance of state-of-the-art large aperture imaging sensor systems, specifically those intended for long range ground to ground target identification. Simple and cost-effective mitigation solutions which operate in real-time are desired. Software-based post-processing techniques are attractive as they lend themselves to easy implementation and integration into the back-end of existing sensor systems. Recently, various post-processing algorithms to mitigate turbulence have been developed and implemented in real-time hardware. To determine their utility in Army-relevant tactical scenarios, an assessment of the impact of the post processing on observer performance is required. In this paper, we test a set of representative turbulence mitigation algorithms on field collected data of human targets carrying various handheld objects in varying turbulence conditions. We use a controlled human perception test to assess handheld weapon identification performance before and after turbulence mitigation post-processing. In addition, novel image analysis tools are implemented to estimate turbulence strength from the scene. Results of this assessment will lead to recommendations on cost-effective turbulence mitigation strategies suitable for future sensor systems.

Infrared imagery over long ranges is hampered by atmospheric turbulence effects, leading to spatial resolutions worse than expected by a diffraction limited sensor system. This diminishes the recognition range and it is therefore important to compensate visual degradation due to atmospheric turbulence. The amount of turbulence is spatially varying due to anisoplanatic conditions, while the isoplanatic angle varies with atmospheric conditions. But also the amount of turbulence varies significantly in time. In this paper a method is proposed that performs turbulence compensation using a patch-based approach. In each patch the turbulence is considered to be approximately spatially and temporally constant. Our method utilizes multi-frame super-resolution, which incorporates local registration, fusion and deconvolution of the data and also can increase the resolution. This makes our method especially suited to use under anisoplanatic conditions. In our paper we show that our method is capable of compensating the effects of mild to strong turbulence conditions.

High fidelity simulations of IR signatures and imagery tend to be slow and do not have effective support for animation of characters. Simplified rendering methods based on computer graphics methods can be used to overcome these limitations. This paper presents a method to combine these tools and produce simulated high fidelity thermal IR data of animated people in terrain. Infrared signatures for human characters have been calculated using RadThermIR. To handle multiple character models, these calculations use a simplified material model for the anatomy and clothing. Weather and temperature conditions match the IR-texture used in the terrain model. The calculated signatures are applied to the animated 3D characters that, together with the terrain model, are used to produce high fidelity IR imagery of people or crowds. For high level animation control and crowd simulations, HLAS (High Level Animation System) has been developed. There are tools available to create and visualize skeleton based animations, but tools that allow control of the animated characters on a higher level, e.g. for crowd simulation, are usually expensive and closed source. We need the flexibility of HLAS to add animation into an HLA enabled sensor system simulation framework.

MR-i is an imaging Fourier-Transform spectro-radiometer. This field instrument generates spectral datacubes in the MWIR and LWIR. It is designed to acquire the spectral signatures of rapidly evolving events. The spectroradiometer is modular. The two output ports of the instrument can be populated with different combinations of detectors (imaging or not). For instance, to measure over a broad spectral range one output port can be equipped with a LWIR camera while the other port is equipped with a MWIR camera. No dichroic filters are used to split the bands, hence enhancing the sensitivity. Both ports can be equipped with cameras imaging the same spectral range but set at different sensitivity levels in order to increase the measurement dynamic range and avoid saturation of bright parts of the scene while simultaneously obtaining good measurements of the faintest parts of the scene. Various telescope options can be used for the input port.

We have looked at applying various image restoration techniques used in astronomy to the problem of imaging through horizontal-path turbulence. The input data comes from an imaging test over a 2.5km path. The point-spread function (PSF) is estimated directly from the data and supplied to the deconvolution algorithms. We show the usefulness of using this approach, together with the analytical form of the turbulent PSF due to D. Fried, for reference-less imaging scenarios.

Using post-processing filters to enhance image detail, a process commonly referred to as boost, can significantly affect the performance of an EO/IR system. The US Army's target acquisition models currently use the Targeting Task Performance (TTP) metric to quantify sensor performance. The TTP metric accounts for each element in the system including: blur and noise introduced by the imager, any additional post-processing steps, and the effects of the Human Visual System (HVS). The current implementation of the TTP metric assumes spatial separability, which can introduce significant errors when the TTP is applied to systems using non-separable filters. To accurately apply the TTP metric to systems incorporating boost, we have implement a two-dimensional (2D) version of the TTP metric. The accuracy of the 2D TTP metric was verified through a series of perception experiments involving various levels of boost. The 2D TTP metric has been incorporated into the Night Vision Integrated Performance Model (NV-IPM) allowing accurate system modeling of non-separable image filters.

Unmanned Aircraft Systems (UAS) have been widely applied into military reconnaissance and surveillance by exploiting the information collected from the digital imaging payload. However, the data analysis of UAS videos is frequently limited by motion blur; the frame-to-frame movement induced by aircraft roll, wind gusts, and less than ideal atmospheric conditions; and the noise inherent within the image sensors. Therefore, the super-resolution mosaicking on low-resolution UAS surveillance video frames, becomes an important task for UAS video processing and is a pre-step for further effective image understanding. Here we develop a novel super-resolution framework which does not require the construction of sparse matrices. This method applied image operators in spatial domain and adopted an iterated back-projection method to conduct super-resolution mosaics from UAS surveillance video frames. The Steepest Descent method, Conjugate Gradient method and Levenberg Marquardt algorithm are used to numerically solve the nonlinear optimization problem in the modeling of super-resolution mosaic. A quantity comparison in computation time and visual performance of the super-resolution using the three numerical methods is performed. The Levenberg Marquardt algorithm provides a numerical solution to the least squares curve fitting, which avoids the time-consuming computation of the inverse of the pseudo Hessian matrix in regular singular value decomposition (SVD). The Levenberg Marquardt method, interpolating between the Gauss-Newton algorithm (GNA) and the method of gradient descent, is efficient, robust, and easy to implement. The results obtained in our simulations shows a great improvement of the resolution of the low resolution mosaic of up to 47.54 dB for synthetic images, and a considerable visual improvement in sharpness and visual details for real UAS surveillance frames. The convergence is generally reached in no more than ten iterations.

The battlefield has shifted from armored vehicles to armed insurgents. Target acquisition (identification, recognition, and detection) range performance involving humans as targets is vital for modern warfare. The acquisition and neutralization of armed insurgents while at the same time minimizing fratricide and civilian casualties is a mounting concern. U.S. Army RDECOM CERDEC NVESD has conducted many experiments involving human targets for infrared and reflective band sensors. The target sets include human activities, hand-held objects, uniforms & armament, and other tactically relevant targets. This paper will define a set of standard task difficulty values for identification and recognition associated with human target acquisition performance.

The search problem discussed in this paper is easily stated: given search parameters (Ρ_∞, τ) that are known functions of time, calculate how the probability of a single observer to acquire a target grows with time. This problem was solved analytically in a previous paper. To investigate the validity of the solution, videos generated using NVIG software show the view from a vehicle traveling at two different speeds along a flat, straight road. Small, medium and large sized equilateral triangles with the same gray level as the road but without texture were placed at random positions on a textured road and military observers were tasked to find the targets. Analysis of this video in perception experiments yields experimental probability of detection as a function of time. Static perception tests enabled Ρ_∞ and τ to be measured as a function of range for the small, medium and large triangles. Since range is a known function of time, Ρ_∞ and τ were known as functions of time. This enabled the calculation of modeled detection probabilities which were then compared with measured detection probabilities.

Modern IR cameras are increasingly equipped with built-in advanced (often non-linear) image and signal processing algorithms (like fusion, super-resolution, dynamic range compression etc.) which can tremendously influence performance characteristics. Traditional approaches to range performance modeling are of limited use for these types of equipment. Several groups have tried to overcome this problem by producing a variety of imagery to assess the impact of advanced signal and image processing. Mostly, this data was taken from classified targets and/ or using classified imager and is thus not suitable for comparison studies between different groups from government, industry and universities. To ameliorate this situation, NATO SET-140 has undertaken a systematic measurement campaign at the DGA technical proving ground in Angers, France, to produce an openly distributable data set suitable for the assessment of fusion, super-resolution, local contrast enhancement, dynamic range compression and image-based NUC algorithm performance. The imagery was recorded for different target / background settings, camera and/or object movements and temperature contrasts. MWIR, LWIR and Dual-band cameras were used for recording and were also thoroughly characterized in the lab. We present a selection of the data set together with examples of their use in the assessment of super-resolution and contrast enhancement algorithms.

Registering two images produced by two separate imaging sensors having different detector sizes and fields of view requires one of the images to undergo transformation operations that may cause its overall quality to degrade with regards to visual task performance. This possible change in image quality could add to an already existing difference in measured task performance. Ideally, a fusion algorithm would take as input unaltered outputs from each respective sensor used in the process. Therefore, quantifying how well an image fusion algorithm performs should be base lined to whether the fusion algorithm retained the performance benefit achievable by each independent spectral band being fused. This study investigates an identification perception experiment using a simple and intuitive process for discriminating between image fusion algorithm performances. The results from a classification experiment using information theory based image metrics is presented and compared to perception test results. The results show an effective performance benchmark for image fusion algorithms can be established using human perception test data. Additionally, image metrics have been identified that either agree with or surpass the performance benchmark established.

This paper presents an image-based system performance model. The image-based system model uses an image metric to compare a given degraded image of a target, as seen through the modeled system, to the set of possible targets in the target set. This is repeated for all possible targets to generate a confusion matrix. The confusion matrix is used to determine the probability of identifying a target from the target set when using a particular system in a particular set of conditions. The image metric used in the image-based model should correspond closely to human performance. The image-based model performance is compared to human perception data on Contrast Threshold Function (CTF) tests, naked eye Triangle Orientation Discrimination (TOD), and TOD including an infrared camera system. Image-based system performance modeling is useful because it allows modeling of arbitrary image processing. Modern camera systems include more complex image processing, much of which is nonlinear. Existing linear system models, such as the TTP metric model implemented in NVESD models such as NV-IPM, assume that the entire system is linear and shift invariant (LSI). The LSI assumption makes modeling nonlinear processes difficult, such as local area processing/contrast enhancement (LAP/LACE), turbulence reduction, and image fusion.

For many military operations situational awareness is of great importance. This situational awareness and related tasks such as Target Acquisition can be acquired using cameras, of which the resolution is an important characteristic. Super resolution reconstruction algorithms can be used to improve the effective sensor resolution. In order to judge these algorithms and the conditions under which they operate best, performance evaluation methods are necessary. This evaluation, however, is not straightforward for several reasons. First of all, frequency-based evaluation techniques alone will not provide a correct answer, due to the fact that they are unable to discriminate between structure-related and noise-related effects. Secondly, most super-resolution packages perform additional image enhancement techniques such as noise reduction and edge enhancement. As these algorithms improve the results they cannot be evaluated separately. Thirdly, a single high-resolution ground truth is rarely available. Therefore, evaluation of the differences in high resolution between the estimated high resolution image and its ground truth is not that straightforward. Fourth, different artifacts can occur due to super-resolution reconstruction, which are not known on forehand and hence are difficult to evaluate. In this paper we present a set of new evaluation techniques to assess super-resolution reconstruction algorithms. Some of these evaluation techniques are derived from processing on dedicated (synthetic) imagery. Other evaluation techniques can be evaluated on both synthetic and natural images (real camera data). The result is a balanced set of evaluation algorithms that can be used to assess the performance of super-resolution reconstruction algorithms.

There have been significant improvements in the image quality metrics used in the NVESD model suite in recent years. The introduction of the Targeting Task Performance (TTP) metric to replace the Johnson criteria yielded significantly more accurate predictions for under-sampled imaging systems in particular. However, there are certain cases which cause the TTP metric to predict optimistic performance. In this paper a new metric for predicting performance of imaging systems is described. This new weighted contrast metric is characterized as a hybrid of the TTP metric and Johnson criteria. Results from a number of historical perception studies are presented to compare the performance of the TTP metric and Johnson criteria to the newly proposed metric.

Electro-optic (EO) images exhibit the properties of high resolution and low noise level, while it is a challenge to distinguish objects with infrared (IR), especially for objects with similar temperatures. In earlier work, we proposed a novel framework for IR image enhancement based on the information (e.g., edge) from EO images. Our framework superimposed the detected edges of the EO image with the corresponding transformed IR image. Obviously, this framework resulted in better resolution IR images that help distinguish objects at night. For our IR image system, we used the theoretical point spread function (PSF) proposed by Russell C. Hardie et al., which is composed of the modulation transfer function (MTF) of a uniform detector array and the incoherent optical transfer function (OTF) of diffraction-limited optics. In addition, we designed an inverse filter based on the proposed PSF to transform the IR image. In this paper, blending the detected edge of the EO image with the corresponding transformed IR image and the original IR image is the principal idea for improving the previous framework. This improved framework requires four main steps: (1) inverse filter-based IR image transformation, (2) image edge detection, (3) images registration, and (4) blending of the corresponding images. Simulation results show that blended IR images have better quality over the superimposed images that were generated under the previous framework. Based on the same steps, the simulation result shows a blended IR image of better quality when only the original IR image is available.

In surveillance applications, the visibility of details within an image is necessary to ensure detection. However, bright spots in images can occupy most of the dynamic range of the sensor, causing lower energy details to appear dark and difficult to see. In addition, shadows from structures such as buildings or bridges obscure features within the image, further limiting contrast. Dynamic range compression and contrast enhancement algorithms can be used to improve the visibility of these low energy details. In this paper, we propose a locally adaptive contrast enhancement algorithm based on the multi-scale wavelet transform to compress the dynamic range of images as well as increase the visibility of details obscured by shadows. Using an edge detector as the mother wavelet, this algorithm operates by increasing the gain of low energy gradient magnitudes provided by the wavelet transform, while simultaneously decreasing the gain of higher energy gradient magnitudes. Limits on the amount of gain imposed are set locally to prevent the over-enhancement of noise. The results of using the proposed method on aerial images show that this method outperforms common methods in its ability to enhance small details while simultaneously preventing ringing artifacts and noise over-enhancement.

The purpose of military camouflage is to make an object hard to see, or to confuse a hostile observer as to its true nature. Perfect camouflage would do this by making itself invisible against its surroundings. Currently, the best camouflage attempts to make the target appear to be a natural part of the background. An imperfect but still very useful metric of this similarity between target and background is the at-range contrast difference between them, and the smaller that is, the harder it is to discern the camouflaged object and the longer it takes to determine its true nature. The intrinsic contrast difference in the reflective wavebands (i.e., visible through short wave infrared), is a function of the spectral nature of the scene illumination and the spectral reflectivity of the camouflage and background. Until recently, military camouflages have been typically designed to work best in the visible band against one of the generic background types such as woodland, desert, arctic, etc., without significant attention paid to performance against a different background, type of scene illumination, or different waveband. This paper documents an investigation into the dependence of the contrast difference behavior of camouflage as a function of waveband, background, and scene illumination using battle dress uniforms (BDU) as material.

TALOS (Transportable and Autonomous Land bOrder Surveillance system - www.talos-border.eu) is an international research project co-funded from EU 7th Framework Program funds in Security priority. The main objective of TALOS project is to develop and field test the innovative concept of a mobile, autonomous system for protecting European land borders. Unmanned Ground Vehicles (UGVs) are major components of TALOS project. The UGVs will be equipped with long range radar for detection of moving vehicle and people, as well as long focal length EO/IR sensors allowing the operator to recognize and identify the detected objects of interest. Furthermore medium focal length IR sensors are used to allow the operator to drive the UGV. Those sensors must fulfill mission requirements for extremely various environmental conditions (backgrounds, topographic characteristics, climatic conditions, weather conditions) existing from Finland in the North and Bulgaria / Turkey in the South of Europe. An infrared sensor performance model was developed at ONERA in order to evaluate target detection, recognition and identification range for several simulations cases representative of the whole environmental variability domain. Results analysis allows assessing the operability domain of the infrared sensors. This paper presents the infrared sensor performance evaluation methodology and the synthesis of a large number of simulation results applied to two infrared sensors of interest: a medium / long range cooled MWIR sensor for observation and a short / medium uncooled LWIR sensor for navigation.

In law enforcement and security applications, the acquisition of face images is critical in producing key trace evidence for the successful identication of potential threats. In this work we, first, use a near infrared (NIR) sensor designed with the capability to acquire images at middle-range stand-off distances at night. Then, we determine the maximum stand-off distance where face recognition techniques can be utilized to efficiently recognize individuals at night at ranges from 30 to approximately 300 ft. The focus of the study is on establishing the maximum capabilities of the mid-range sensor to acquire good quality face images necessary for recognition. For the purpose of this study, a database in the visible (baseline) and NIR spectrum of 103 subjects is assembled and used to illustrate the challenges associated with the problem. In order to perform matching studies, we use multiple face recognition techniques and demonstrate that certain techniques are more robust in terms of recognition performance when using face images acquired at different distances. Experiments show that matching NIR face images at longer ranges (i.e. greater than about 300 feet or 90 meters using our camera system) is a very challenging problem and it requires further investigation.

In this paper the effects of the internal temperature on the response of uncooled microbolometer cameras have been studied. To this end, different temperature profiles steering the internal temperature of the cameras have been generated, and black-body radiator sources have been employed as time and temperature constant radiation inputs. The analysis conducted over the empirical data has shown the existence of statistical correlation between camera's internal temperature and the fluctuations in the read-out data. Thus, when measurements of the internal temperature are available, effective methods for compensating the fluctuations in the read-out data can be developed. This claim has been tested by developing a signal processing scheme, based on a polynomial model, to compensate for the output of infrared cameras equipped with amorphous-Silicon and Vanadium-Oxide microbolometers.

The most important adverse impact on the Electronic Warfare (EW) simulation is that the number of signal sources that can be tested simultaneously is relatively small. When the number of signal sources increases, the analog hardware, complexity and costs grow by the order of N², since the number of connections among N components is O(N*N) and the signal communication is bi-directional. To solve this problem, digitization of the signal is suggested. In digitizing a radiofrequency signal, an Analog-to-Digital Converter (ADC) is widely used. Most research studies on ADCs are conducted from designer/test engineers' perspective. Some research studies are conducted from market's perspective. This paper presents a generic way to select, evaluate and test ultra high bandwidth COTS ADCs and generate requirements for digitizing continuous time signals from the perspective of user's needs. Based on user's needs, as well as vendor's published, ideal and actual specifications, a decision can be made in selecting a proper ADC for an application. To support our arguments and illustrate the methodology, we evaluate a Tektronix TADC-1000, an 8-bit and 12 gigasamples per second ADC. This project is funded by JEWEL lab, NAWCWD at Point Mugu, CA.

TTP (Targeting Task Performance) model is widely used for the estimation of theoretical performance of observation devices. It is used, for example, in the NVTERM software and makes it possible to determine the detection, recognition and identification ranges for a standard target types on the basis of known technical parameters of analyzed device. Many theoretical analysis concerning TTP model can be found, as well as a few experimental, field test results. However the usability of the TTP model for the calculation of range parameters on the basis of laboratory test results has not been widely analyzed. The paper presents an attempt to apply TTP model for the estimation of range parameters of thermal cameras using laboratory measurement results of camera properties. The test stand consists of an IR collimator, a standard IR source, a set of test targets and a computer with data acquisition card. The method used for the measurement of aforementioned characteristics will be described and the algorithms used to finally estimate the range parameters of a tested thermal camera using TTP model.

The paper presents results of testing the infrared image quality enhancing algorithm based on histogram processing. Testing were performed on real images registered in NIR, MWIR, and LWIR spectral bands. Infrared images are a very specific type of information. The perception and interpretation of such image depends not only on radiative properties of observed objects and surrounding scenery. Probably still most important are skills and experience of an observer itself. In practice, the optimal settings of the camera as well as automatic temperature range or contrast control do not guarantee the displayed images are optimal from observer's point of view. The solution to this are algorithms of image quality enhancing based on digital image processing methods. Such algorithms can be implemented inside the camera or applied later, after image registration. They must improve the visibility of low-contrast objects. They should also provide effective dynamic contrast control not only across entire image but also selectively to specific areas in order to maintain optimal visualization of observed scenery. In the paper one histogram equalization algorithm was tested. Adaptive nature of the algorithm should assure significant improvement of the image quality and the same effectiveness of object detection. Another requirement and difficulty is that it should also be effective for any given thermal image and it should not cause a visible image degradation in unpredictable situations. The application of tested algorithm is a promising alternative to a very effective but complex algorithms due to its low complexity and real time operation.

The aim of this research is to experimentally validate a Gauss-Markov model, previously developed by our group, for the non-uniformity parameters of infrared (IR) focal plane arrays (FPAs). The Gauss-Markov model assumed that both, the gain and the offset parameters at each detector, are random state-variables modeled by a recursive discrete-time process. For simplicity, however, we have regarded here the gain parameter as a constant and assumed that solely the offset parameter follows a Gauss-Markov model. Experiments have been conducted at room temperature and IR data was collected from black-body radiator sources using microbolometer-based IR cameras operating in the 8 to 12 μm. Next, well-known statistical techniques were used to analyze the offset time series and determinate whether the Gauss-Markov model truly fits the temporal dynamics of the offset. The validity of the Gauss-Markov model for the offset parameter was tested at two time scales: seconds and minutes. It is worth mentioning that the statistical analysis conducted in this work is a key in providing mechanisms for capturing the drift in the fixed pattern noise parameters.

Every naval vessel can be detected and identified on the basis of its characteristics. The reduction of signature or matching it to the surrounding environment are one of the key tasks regarding survivability on a modern battlefield. The typical coatings applied on the outer surfaces of vessels are various kinds of paints. Their purpose is to protect the hull from aggressive sea environment and to provide camouflage in the visual spectrum as well as scatter and deflect microwave radiation. Apart from microwave and visual, infrared is most important spectral band used for detection purposes. In order to obtain effective protection in infrared the thermal signature of a vessel is required. It is determined on the basis of thermal contrast between a vessel itself and actual background and depends mostly on radiant properties of the hull. Such signature can be modified by altering apparent temperature values or the directions, in which the infrared radiation is emitted. The paper discusses selected methods of modification of vessel's infrared signature and effectiveness of infrared camouflage. Theoretical analyses were preceded by experimental measurements. The measurement-class infrared cameras and imaging spectroradiometers were used in order to determine the radiant exitance from different surface types. Experiments were conducted in selected conditions taking into account solar radiation and radiation reflected from elements of the surrounding scenery. Theoretical analysis took into account radiant angular properties of a vessel hull and attenuation of radiation after passing through the atmosphere. The study was performed in MWIR and LWIR ranges.

Korean Multi-purpose Satellite-3A (KOMPSAT-3A), which weighing about 1,000 kg is scheduled to be launched in 2013 and will be located at a sun-synchronous orbit (SSO) of 530 km in altitude. This is Korea's rst satellite to orbit with a mid-wave infrared (MWIR) image sensor, which is currently being developed at Korea Aerospace Research Institute (KARI). The missions envisioned include forest re surveillance, measurement of the ocean surface temperature, national defense and crop harvest estimate. In this paper, we shall explain the MWIR scene generation software and atmospheric compensation techniques for the infrared (IR) camera that we are currently developing. The MWIR scene generation software we have developed taking into account sky thermal emission, path emission, target emission, sky solar scattering and ground re ection based on MODTRAN data. Here, this software will be used for generating the radiation image in the satellite camera which requires an atmospheric compensation algorithm and the validation of the accuracy of the temperature which is obtained in our result. Image visibility restoration algorithm is a method for removing the eect of atmosphere between the camera and an object. This algorithm works between the satellite and the Earth, to predict object temperature noised with the Earth's atmosphere and solar radiation. Commonly, to compensate for the atmospheric eect, some softwares like MODTRAN is used for modeling the atmosphere. Our algorithm doesn't require an additional software to obtain the surface temperature. However, it needs to adjust visibility restoration parameters and the precision of the result still should be studied.

Infrared imaging allows surveillance during the night, thus it has been widely used for military and security applications. However, infrared images are generally characterized by low resolution, low contrast, and an unclear texture with no color information. Moreover, various types of noises and background clutters can degrade the image quality. This paper discusses multi-level segmentation for infrared images. The expectation-maximization algorithm is adopted to cluster pixels on the basis of Gaussian mixture models. The use of the multi-level segmentation method enables the extraction of human target regions from the background of the image. Several infrared images are processed to demonstrate the effectiveness of the presented method.

Seekers are one of the most important subsystems of guided aerial munitions such that they are used both to detect and track prespecified targets within specific engagement scenarios. Among them, infrared (IR) types constitute a significant portion of seekers. Actually, performance characteristics of seekers depend on some certain factors. Regarding the type of their sources, these factors can be classified as internal and external factors. Sensitivity, resolution, optics,detectors, dome geometry, and materials happen to the most significant internal factors acting on the IR seekers while atmospheric transmittance, and visibility can be counted within the remarkable external factors. In this study, the basic effects of the above mentioned internal and external factors on the performance characteristics of a generic IR seeker is examined and corresponding interpretations are presented at the end of the work.