High Frequency Edge Information-based Sampling Algorithm for Multi-scale Block Compressive Sensing

Abstract

The same sampling rate is applied to all subblocks of the same layer, which results in the deviation of the information contained in the sampling observation in the multi-scale block compressive sensing algorithm from the real information of the original image, thus affecting the accuracy of image reconstruction. For this reason, the multi-scale block compressive sensing adaptive sampling method (HEM) using HF edge information is proposed. In this method, the edge information is extracted by using the high frequency coefficients of wavelet transform, and then the edge information of the subblock of wavelet decomposition graph is calculated, and then converted into the adaptive sampling rate of each subblock. The influence of different decomposition layers and block sizes on HEM method is discussed through testing in 6 standard images. The experimental results show that the proposed algorithm can improve the PSNR of the reconstructed image at the same sampling rate on standard test images and cable trench inspection images. Among them, Stanwick image(S=0.1 and S=0.2), Russland image(S=0.3) and Stomach CT image(S=0.4) are partial exceptions.