Non-convex compression perception optimization reconstruction method based on sketch representation and structured clustering

Abstract

The invention discloses a non-convex compression preception optimization reconstruction method based on sketch representation and structured clustering. The method mainly settles a problem of inaccurate compression image reconstruction on the condition of low sampling rate. The method comprises the following steps of according to a sketch of an image, defining a sketchable block and a non-sketchable block, wherein the non-sketchable block comprises a smooth block and a patterned block, and the sketchable block comprises a unidirectional block and a multidirectional block; performing clustering based on sketching direction guidance on the unidirectional block; performing clustering based on a direction distribution characteristic on the multidirectional block; performing gray scale clustering on the smooth block and the pattern block; performing multi-measuring-vector observation on each kind of image blocks; and in reconstruction, executing a particle swarm optimization algorithm based on crossing and atom direction restraint according to multiple measuring matrixes, kind index and direction information of each kind of image blocks for obtaining a final reconstructed image. Compared with a TS-RS method and an NR-DG method, the non-convex compression perception optimization reconstruction method has advantages of high quality of the reconstructed image, high robustness and high suitability for reconstruction of a natural image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a non-convex compressive sensing optimization reconstruction method based on sketch representation and structured clustering, which can be used to reconstruct natural images. Background technique [0002] In recent years, a new data theory compressive sensing CS has emerged in the field of signal processing. This theory realizes compression while collecting data, breaks through the limitations of the traditional Nyquist sampling theorem, and brings new advantages to data collection technology. The revolutionary changes make 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. Designing effective observation and reconstruction methods is an ...

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Problems solved by technology

Although this method judges the structure type and direction of the image block, it is not accurate in the case of low sampling rate, which leads to the lack of accuracy and robustness of reconstruction at low sampling rate.

[0005] At the same time, both of the above two methods have the disadvantage of slow reconstruction speed. Both of them are based on the two-stage optimization of genetic optimization algorithm and clone selection algorithm, which is slow and not conducive to real-time application.

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