Overall situation reconstitution optimization model construction method for image block compressed sensing

Abstract

The invention discloses an overall situation reconstitution optimization model construction method for image block compressed sensing. The procedures of a collecting end include that firstly, an image x is divided into n B*B small blocks xi, wherein x and xi are pulled to be column vectors in a raster scanning manner; secondly, an independent identically distributed gauss random matrix phi B with the size of MB*B2 is generated; thirdly, incoherent measuring is performed on each block xi to obtain observed value vectors yi which is equal to phi B xi; fourthly, the observed value vectors yi and a seed for generating the gauss random matrix are sent to a reconstruction end. The procedures of the reconstruction end include that firstly, the received observed value vectors yi of all the blocks are accumulated to be y=[yi; y2;..., yn] in columns; secondly, an overall situation reconstitution measurement operator theta ( ) is constructed, wherein the input of the overall situation reconstitution measurement operator is an image x, the corresponding output of the overall situation reconstitution measurement operator is y, and the overall situation reconstitution measurement operator is composed of a block measurement matrix set phi and a ranking operator P ( ); thirdly, an overall optimization reconstitution model is set up, and the image is recovered with a corresponding compressed sensing reconstitution algorithm. The overall situation reconstitution optimization model construction method for image block compressed sensing can effectively eliminate the block effect in the prior art, and strong robustness on variation of the block size B is achieved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for constructing an optimized model for image block compression sensing reconstruction, which can be used for reconstructing natural images. Background technique [0002] In traditional imaging systems, natural images are first converted into digital images at a higher sampling rate, and then compressed using JPEG or JPEG2000 to reduce the storage capacity of digital images. However, due to the high computational complexity of such compression coding methods, they are not suitable for low-power, low-resolution, low-computational imaging devices (e.g., sensors for image acquisition in the Internet of Things). In recent years, the Compressed Sensing (CS) theory proposes that the signal can be directly compressed while sampling. The theory is based on the breakthrough work of Candes et al. and Donoho, pointing out that under certain conditions, the sig...

AI Technical Summary

Problems solved by technology

However, the image reconstruction model based on Block CS still has shortcomings: the way of independently reconstructing each block destroys the image features, resulting in block effects

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