Compressed sensing reconstruction method based on image nonlocal similarity

A compressive sensing reconstruction and non-local similarity technology, applied in the field of image processing, can solve problems such as unsatisfactory image reconstruction effect, high computational complexity, and false information

Inactive Publication Date: 2016-04-20
ZHEJIANG UNIV
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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
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  • Compressed sensing reconstruction method based on image nonlocal similarity
  • Compressed sensing reconstruction method based on image nonlocal similarity
  • Compressed sensing reconstruction method based on image nonlocal similarity

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Embodiment Construction

[0078] The present invention will be further described below in conjunction with accompanying drawing.

[0079] The invention is a compression sensing reconstruction method based on non-local similarity of images. The invention effectively combines the non-local similarity of the image, the low-rank matrix and the minimum total variation (TV), adopts a new similar block matching method, and finally obtains a high-quality reconstructed image. Whole flow chart of the present invention is attached figure 1 As shown, it mainly includes several steps such as similar block matching, low-rank matrix recovery and minimum total variation constraints. The specific implementation steps are as follows:

[0080] Step 1. Initial recovery

[0081] According to the compressed sensing theory, for the original signal x with dimension N, according to the observation matrix H∈R with a certain structure M×N (MM×1 , the original signal can be reconstructed accurately or approximately with high ...

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Abstract

The invention puts forward a compressed sensing reconstruction method based on image nonlocal similarity. According to the method, image nonlocal similarity, a low-rank matrix and minimum total variation (TV) are combined, and two priors, namely, the local similarity and local smoothness of images, are fully utilized. On one hand, the block effect and the loss of global structure information caused by independent processing of single image blocks in the traditional method are eliminated, and on the other hand, real details of images are retained and false details produced by unreliable information are reduced or removed while noise is suppressed. High-quality compressed sensing reconstruction of images is realized. Compared with a general reconstruction method based on transform domain sparse or TV constraints, the method of the invention is of robustness to noise, better reconstruction quality is achieved, and great improvement is achieved both in visual effect and evaluation index.

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...

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Application Information

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IPC IPC(8): G06T5/00G06T7/00
CPCG06T5/001
Inventor 陈跃庭黄芝娟徐之海李奇冯华君
Owner ZHEJIANG UNIV
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