Signal Reconstruction Based on Block Compressed Sensing

Compressed sensing (CS) is a new area of signal processing for simultaneous signal sampling and compression. The CS principle can reduce the computation complexity at the encoder side and transmission costs, but has huge computation load at the decoder. In this paper, a simple block-based compressed sensing reconstruction for still images is proposed. Firstly, original image is divided into small blocks, and each block is sampled independently. Secondly, the image block is divided into flat and non-flat block, and processed with different ways. Finally, mean filter and an improvement total-variation (TV) method is sued to optimize image. Simulation results show that the proposed algorithm can effectively remove the blocking artifacts and reduce the computation complexity.