Adaptive reweighted compressed sensing for image compression

According to the compressed sensing (CS) theory, a signal that is sparse in a certain domain can be nearly exactly recovered from a few measurements where the sampling rate is lower than the Nyquist rate. This theory has been successfully applied to the image compression in the past few years as most image signals are highly sparse. In this paper, we apply an adaptive sampling mechanism to the reweighted block-based CS (BCS). The proposed adaptive sampling allocates the measurements to each image block according to the statistical information of the block so as to sample and recover the image more efficiently. Experimental results demonstrate that our adaptive reweighted method offers a very significant quality improvement compared with the traditional BCS schemes, including the non-reweighted and reweighted ones.