An image compression method for space multispectral time delay and integration charge coupled device camera

Li, Jin; Jin, Lei; Ran-feng, Zhang

doi:10.1088/1674-1056/22/6/064203

Chinese Phys. B

2013

DOI: 10.1088/1674-1056/22/6/064203

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An image compression method for space multispectral time delay and integration charge coupled device camera

Jin Li

Lei Jin

Zhang Ran-feng

Abstract: An image compression method for space multispectral time delay and integration charge coupled device camera * Li Jin(李进) a)b) † , Jin Long-Xu(金龙旭) b) , and Zhang Ran-Feng(张然峰) b) a)

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Cited by 2 publications

References 17 publications

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Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Liu

2019

Remote Sensing

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A multispectral image is a three-order tensor since it is a three-dimensional matrix, i.e.one spectral dimension and two spatial position dimensions. Multispectral image compression canbe achieved by means of the advantages of tensor decomposition (TD), such as NonnegativeTucker Decomposition (NTD). Unfortunately, the TD suffers from high calculation complexity andcannot be used in the on-board low-complexity case (e.g., multispectral cameras) that the hardwareresources and power are limited. Here, we propose a low-complexity compression approach formultispectral images based on convolution neural networks (CNNs) with NTD. We construct anew spectral transform using CNNs, where the CNNs are able to transform the three-dimensionspectral tensor from large-scale to a small-scale version. The NTD resources only allocate thesmall-scale three-dimension tensor to improve calculation efficiency. We obtain the optimizedsmall-scale spectral tensor by the minimization of original and reconstructed three-dimensionspectral tensor in self-learning CNNs. Then, the NTD is applied to the optimized three-dimensionspectral tensor in the DCT domain to obtain the high compression performance. We experimentallyconfirmed the proposed method on multispectral images. Compared to the case that the newspectral tensor transform with CNNs is not applied to the original three-dimension spectral tensorat the same compression bit-rates, the reconstructed image quality could be improved. Comparedwith the full NTD-based method, the computation efficiency was obviously improved with only asmall sacrifices of PSNR without affecting the quality of images.

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Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Liu

2019

Remote Sensing

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Analysis on the simplified optic coma effect on spectral image inversion of coded aperture spectral imager

Liu¹,

Lv²,

Ge³

et al. 2015

Acta Phys. Sin.

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With the novel spectrum imaging technology development in recent years, the push-broom coded-aperture spectral imaging (PCASI) shows the advantages of high throughput, high SNR, high stability etc. This coded-aperture spectral imaging utilizes fixed-code templates and push-broom mode, which can realize high-precision reconstruction of spatial and spectral information. But during optical lens designing, manufacturing and debugging, there inevitably exist some minor coma errors. Even minor coma errors can reduce image quality. In this paper, we simulate the system optical coma error which influences the quality of reconstructed image, analyze the variantion of the coded aperture in different optical coma effect, and then propose an accurate curve for image quality and optic coma quality in 255× 255 coded template, which provides important references for the design and development of push-broom coded-aperture spectrometer.

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An image compression method for space multispectral time delay and integration charge coupled device camera

Abstract: An image compression method for space multispectral time delay and integration charge coupled device camera * Li Jin(李进) a)b) † , Jin Long-Xu(金龙旭) b) , and Zhang Ran-Feng(张然峰) b) a)

Cited by 2 publications

References 17 publications

Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Analysis on the simplified optic coma effect on spectral image inversion of coded aperture spectral imager

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An image compression method for space multispectral time delay and integration charge coupled device camera

Abstract: An image compression method for space multispectral time delay and integration charge coupled device camera * Li Jin(李 进) a)b) † , Jin Long-Xu(金龙旭) b) , and Zhang Ran-Feng(张然峰) b) a)

Cited by 2 publications

References 17 publications

Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Analysis on the simplified optic coma effect on spectral image inversion of coded aperture spectral imager

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Product

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Abstract: An image compression method for space multispectral time delay and integration charge coupled device camera * Li Jin(李进) a)b) † , Jin Long-Xu(金龙旭) b) , and Zhang Ran-Feng(张然峰) b) a)