A Compressed Sensing Reconstruction Method Based on Non-local Similarity of Images

Abstract

The present invention proposes a compressed sensing reconstruction method based on image non-local similarity. This invention combines image non-local similarity, low-rank matrix and minimum total variation (TV), making full use of the two priors of local similarity and local smoothness of the image. On the one hand, it eliminates the traditional method based on a single image patch. Blocking effects and loss of global structural information caused by independent processing, on the other hand, while suppressing noise, it can well maintain the real details of the image and reduce or remove false details generated by unreliable information. The present invention realizes high-quality compressed sensing reconstruction of images. Compared with general reconstruction methods based on transform domain sparsity or TV constraints, the method of the present invention is robust to noise and can obtain better reconstruction quality. , the visual effects and evaluation indicators have been greatly improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a compressive sensing reconstruction method based on non-local similarity of images. Background technique [0002] The higher the resolution of the image, the richer the details it contains. High-resolution images are of great significance in practical applications. However, the massive data generated by high-resolution images limits the further improvement of image resolution to some extent. The traditional compression sampling method must obey the Nyquist sampling law, that is, the sampling data can completely retain the information of the original signal when the sampling frequency is greater than twice the highest frequency of the signal, and the compressed sensing theory breaks through the limitation of the Nyquist frequency , it can measure at a sampling rate much lower than Nyquist's theorem, and reconstruct the original signal exactly or approximately through a r...

AI Technical Summary

Problems solved by technology

The sparse prior and image TV prior based on the fixed domain have a good approximation effect for the signal with smooth slices, but the reconstruction effect for the image with rich texture information is not ideal, the texture feature will be smoothed during the reconstruction process, and it is possible generate false information

In 2006, Elad et al. took the lead in proposing a machine learning-based adaptive dictionary (i.e., sparse base) construction method, which uses an adaptive dictionary instead of a fixed sparse base. Although the sparsity of image blocks is fully considered, dictionary training is a big problem. Scale non-convex optimization problem, high computational complexity

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