Block-based variable density compressed image sampling

Wei Qiao, Bin Liu, Zixiang Xiong, Gonzalo R. Arce, Javier Garcia-Frías, Wenwu Zhu, Zhisheng Yan

Abstract: Compressed sampling (CS) is a technique that enables signal reconstruction at sub-Nyquist sampling rate. A key problem in CS is how to design the sampling scheme. In this paper, we propose a novel sampling method for compressed image sampling, which exploits a priori information and uses a block-based strategy to improve image reconstruction. Our block-based sampling scheme assigns more samples to blocks with more high-frequency contents while making sure that important coefficients of each block are sampled. Simulation results show that our proposed method outperforms existing methods on both reconstruction quality and running time.