Compressed sensing image reconstruction method based on principal component analysis (PCA) redundant dictionary and direction information

Abstract

The invention discloses a compressed sensing image reconstruction method based on a principal component analysis (PCA) redundant dictionary and direction information. The compressed sensing image reconstruction method based on the PCA redundant dictionary and the direction information mainly solves the problem that in an existing compressed sensing reconstruction method OMP, a reconstructed image under a blocking compressed sensing framework has blocking effect and fuzzy texture. The compressed sensing image reconstruction method based on the PCA redundant dictionary and the direction information comprises the following steps: constructing the PCA redundant dictionary; receiving measurement matrixes and blocking measurement vector quantities, and judging category of an image block to be reconstructed according to each blocking measurement vector quantity; designing a species group initialization scheme and a sequencing cross operator based on the direction information on each image block to be reconstructed, and using a genetic algorithm and a clone selection algorithm to achieve reconstruction of each image block under the PCA redundant dictionary. Compared with an OMP method, the compressed sensing image reconstruction method based on the PCA redundant dictionary and the direction information has the advantages of being capable of seeking an optimum sparse representation of each image block from the overall situation under the PCA redundant dictionary, clear in texture and edge of the reconstructed image, and capable of being used for acquiring a high quality image in the process of reconstructing images under the blocking compressed sensing framework.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a compressed sensing image reconstruction method, which can be used to obtain a high-definition image when restoring the original image. 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 sparse representation, commonly used dictionaries include cosine dictionary, ridgelet dictionary, etc. In terms of signal reconstruction, by solving l 0 or l 1 Norm optimization problem to reconstruct images. [0003] Tropp et al. proposed a random obser...

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Problems solved by technology

[0006] The purpose of the present invention is to address the shortcomings of the existing compressed sensing reconstruction technology that the greedy algorithm cannot effectively sparsely represent the image signal when the number of observations is small, resulti

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