An image compressed sensing reconstruction algorithm based on non-local low rank and total variation

A reconstruction algorithm and image compression technology, which is applied in the field of signal processing, can solve problems such as the introduction of wrong texture edge artifacts in reconstructed images, weak anti-noise ability of the algorithm, and susceptibility to noise, so as to achieve enhanced adaptability and reconstruction The effect of performance and strong robustness

Pending Publication Date: 2019-04-05
CHONGQING UNIV OF POSTS & TELECOMM
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However, the weighting strategies proposed now generally have the following problems: the weight coefficients contain both low-frequency information and high-frequency information of the image, and the weight coeffic

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  • An image compressed sensing reconstruction algorithm based on non-local low rank and total variation
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  • An image compressed sensing reconstruction algorithm based on non-local low rank and total variation

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[0019] The implementation of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0020] The NLR-CS algorithm proposed by Dong et al. combines the NSS of the image and the low-rank attributes of similar image blocks, and has the characteristics of good reconstruction effect and high efficiency. However, since the NLR-CS algorithm divides images overlappingly, that is, the same pixel can be divided into multiple similar block groups. Therefore, the algorithm needs to reconstruct each low-rank matrix L i The solutions of L are adjusted and aggregated, that is, for all L i (k+1) The average of the solution will blur the edge and structural information of the image, especially in the case of low sampling rate, which will cause the reconstructed image to be too smooth, and the algorithm only uses the single property of the image, and its adaptability is not strong. Therefore, the present invention introduces full variation regu...

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Abstract

In order to accurately and effectively realize compressed sensing reconstruction of a natural image, the invention provides a compressed sensing reconstruction algorithm based on image non-local low rank and total variation. According to the algorithm, non-local self-similarity and local smoothness characteristics of images are considered, a traditional total variation model is improved, weights are only set for high-frequency components of the images, and an edge detection operator with a differential curvature is used for constructing a weight coefficient. Furthermore, the algorithm takes animproved total variation model and a non-local low-rank model as constraints to construct an optimization model; and a smooth non-convex function log det (.) and a soft threshold function are respectively adopted to solve the low-rank and total variation optimization problems, so that the properties of the image are well utilized, the detail information of the image is protected, and the noise resistance and the adaptability of the algorithm are improved.

Description

technical field [0001] The invention belongs to the technical field of signal processing, in particular to an image compression sensing reconstruction algorithm based on non-local low-rank and full variation. Background technique [0002] Compressed Sensing (Compressed Sensing, CS) theory is a new sampling technology proposed in recent years, it can perform coding measurement at a sampling rate much lower than Nyquist sampling theorem, and reconstruct the original signal accurately or approximately through the algorithm, successfully Simultaneous sampling and compression of signals are realized, and problems existing in Nyquist sampling theorem are solved. Its theoretical framework mainly includes three parts: sparse representation, non-adaptive linear measurement and image reconstruction. The precise reconstruction of the signal is the core issue of the CS theory research, and the CS theory points out that the signal is compressible or sparse in a certain domain, which is ...

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Application Information

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IPC IPC(8): G06T9/00
CPCG06T9/001
Inventor 赵辉张静张乐刘莹莉
Owner CHONGQING UNIV OF POSTS & TELECOMM
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