Non-convex compressive sensing image reconstruction method based on evolutionary multi-objective optimization

Abstract

The invention relates to a method for reconstructing a non-convex compression congnitive image based on evolution multi-target optimization. The method is used for reconstructing a medical image and a natural image. The invention discloses a segmentation break-apart compression sampling method, the shortcoming that some image blocks are not sparse or weakly sparse in the segmentation compression congnitive reconstruction technology in the prior art is overcome, and the quality of a reconstructed image is improved. Restricted single target optimization is converted into multi-target optimization enabling the sparseness to also serve as an optimization target, and compression congnitive optimization reconstruction method based on a wavelet domain is achieved by utilizing an evolution multi-target optimization thought for the images, the shortcoming that the sparseness is hard to determine in the existing compression cognitive reconstruction technology is overcome, and the quality of the reconstructed image is improved. According to the method for reconstructing the non-convex compression congnitive image based on evolution multi-target optimization, edge position information of the image serves as position prior for guiding the sparse coefficient solving in an IHT method, and the shortcoming that the sparse coefficient position is less considered in the existing compression congnitive reconstruction technology is overcome, so that the quality of the reconstructed image is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and specifically relates to a non-convex compressive sensing image reconstruction method based on evolutionary multi-objective optimization, which is used for reconstructing medical images and natural images. Background technique [0002] In recent years, a new data acquisition theory "compressed sensing" CS has emerged in the field of signal processing. Revolutionary changes have come, making the theory have broad application prospects in compressed imaging systems, military cryptography, wireless sensing and other fields. Compressed sensing theory mainly includes three aspects: sparse representation of signal, observation of signal and reconstruction of signal. In terms of signal reconstruction, by solving l 0 or l 1 Norm optimization problem to reconstruct images. [0003] In the literature "JoelA.Tropp, AnnaC.Gilbert, SignalRecoveryFromRandomMeasurementsViaOrthogonalMatchingPursu...

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Problems solved by technology

The disadvantage of this method is that it reconstructs a one-dimensional random sparse signal, and does not implement this method for images. Naturally, it does not introduce prior information of images to guide, and IHT does not use position information to guide the solution. The sparsity range is also artificial, resulting in poor reconstructed image quality

[0005] In addition, for the block compression sensing image reconstruction method in the wavelet domain, its advantages are: small amount of calculation, less time-consuming; its disadvantage is: in the wavelet domain, after the high-frequency wavelet coefficients are divided into blocks, it may There will be some blocks that are not sparse or weakly sparse, which violates the theoretical basis of compressed sensing, resulting in poor quality of the reconstructed image

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