Multi-Scale Block Compressed Sensing Algorithm Based on Gray-Level Co-Occurrence Matrix

Aiming at the problem that image edges and contours cannot be accurately reconstructed, a multi-scale block-based compressed sensing algorithm based on gray-level co-occurrence matrix is proposed in this paper. The algorithm uses three-level discrete wavelet transform to decompose the image into high-frequency part and low-frequency part. The entropy of the gray-level co-occurrence matrix is used to analyze the texture complexity of the high-frequency part of the image block, and the image block texture is subdivided and the sampling rate is adaptively allocated. The smooth projection Landweber algorithm is utilized to reconstruct the image and eliminate the blocking effect caused by the block. Compression and reconstruction simulation of various images arc conducted. Experimental results show that when there is no observation noise and the sampling rate is 0.1, the peak signal-to-noise ratio (PSNR) obtained by the algorithm on Mandrill images is 25.37 dB, which is 2.51 dB higher than the existing non-uniform block algorithm. Under different noise levels, the PSNR of the algorithm is only 0.41-2.05 dB lower than that of no noise. For the image with high texture complexity, the reconstruction effect of the algorithm is obviously better than that of non-uniform block algorithm, and has good robustness to noise.