An Adaptive Sampling Method of Compressed Sensing by Combining Spacial Frequency with Direction Features

In the procedure of sampling of block compress sensing, to fully make use of features of image blocks for more reasonable sampling rate, this paper proposes an adaptive sampling algorithm of block- divided compressed sensing for images based on textural feature. First, the spacial frequency is utilized to extract the textural features of image blocks; Second, each block is categorized into the smooth blocks or the textual blocks based on the textual features, and the basic sampling rate is obtained simultaneously; Third, we adjust the subrate of subbands using the statistical characteristics of the coefficients in wavelet domain based on basic sample rate for the textural blocks. Finally, the smooth projected Landweber is employed to reconstruct images. The experiment results show that when the compressing ratio is modest, the quality of reconstructed images can be improved greatly by the proposed algorithm comparing with other block-based compressed sensing algorithms from the aspects of objective indicator and visual effect.