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Proceedings Paper

Convolutional neural network based computational imaging spectroscopy
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Paper Abstract

Computational imaging spectrometry provides spatial-spectral information of objects. This technology has been applied in biomedical imaging, ocean monitoring, military and geographical object identification, etc. Via compressive sensing with coded apertures, 3D spatial-spectral data cube of hyperspectral image is compressed into 2D data array to alleviate the problems due to huge amounts of data. In this paper, a 3D convolutional neural network (3D CNN) is proposed for reconstruction of compressively sensed (CS) multispectral image. This network takes the 2D compressed data as the input and gives an intermediate output, which has identical size with the original 3D data. Then a general image denoiser is applied on it to obtain the final reconstruction result. The network with one fully connected layer, six 3D convolutional layers is trained with a standard hyperspectral image dataset. Though the compression rate is extremely high (16:1), this network performs well both in spectral reconstruction, demonstrated with single point spectrum, and in quantitative comparison with original data, in terms of peak signal to noise ratio (PSNR). Compared with state-of-the-art iterative reconstruction methods e.g. two-step iterative shrinkage/thresholding (TwIST), this network features high speed reconstruction and low spectral dispersion, which potentially guarantees more accurate identification of objects.

Paper Details

Date Published: 17 September 2018
PDF: 7 pages
Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107521T (17 September 2018); doi: 10.1117/12.2319646
Show Author Affiliations
Chenning Tao, Zhejiang Univ. (China)
Xiao Shu, Hang Zhou Brightening Lamp Supervision & Management Ctr. (China)
Wentao Zhang, Zhejiang Univ. (China)
Xiao Tao, Zhejiang Univ. (China)
Chang Wang, Zhejiang Univ. (China)
Zhenrong Zheng, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 10752:
Applications of Digital Image Processing XLI
Andrew G. Tescher, Editor(s)

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