Non-convex compressed sensing image reconstruction method based on redundant dictionary and structure sparsity

Abstract

The invention discloses a non-convex compressed sensing image reconstruction method based on a redundant dictionary and structure sparsity. A reconstruction process of the method includes: observing original image blocks; using a mutual neighboring technology for clustering observation vectors; using a genetic algorithm for finding optimal atom combinations in a dictionary direction for each class of observation vectors, and preserving species; after species expansion operation is executed on each image block, using a clonal selection algorithm for finding an optimal atom combination on scale and displacement in a determined direction for each image block; reconstructing each image block by the optimal atom combination; and piecing all the constructed image blocks in sequence to form an entire constructed image. Image structure sparsity prior and redundant dictionary direction features are fully utilized, the genetic algorithm is combined with the clonal selection algorithm, and the method is used as a nonlinear optimization reconstruction method to realize image reconstruction. The reconstructed image is good in visual effect, high in peak signal noise ratio and structural similarity, and the method can be used for non-convex compressed sensing reconstruction of image signals.

Description

Technical field [0001] The present invention belongs to the technical field of image processing, and further relates to image reconstruction, in particular to a non-convex compressed sensing image reconstruction method based on Ridgelet redundancy dictionary. This method is used to obtain high-definition images when restoring the original image. Background technique [0002] With the rapid development of information technology and science and technology, image processing technology is increasingly applied to people's production and life. Such as the use of satellite images for resource investigation, disaster monitoring, city planning, medical images for disease detection, and industrial images for classification and quality testing of parts. [0003] Due to the huge amount of image data in practical applications, in order to reduce the cost of information storage, processing and transmission, people perform high-speed Nyquist sampling and compression coding on the signal before s...

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Problems solved by technology

[0010] Aiming at the problems in the above-mentioned prior art that the processing of image information cannot converge to the global optimum and the quality of image reconstruction is low, the present invention proposes a non-convex compressiv

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