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

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 ...

AI Technical Summary

Problems solved by technology

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 coefficients are constructed by using the first-order gradient of the image. This weighting method will introduce Wrong textures and edge-like artifacts, and are susceptible to noise, making the algorithm weak against noise

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