A RE-CONSTRUCTIVE ALGORITHM TO IMPROVE IMAGE RECOVERY IN COMPRESSED SENSING

摘要

In this paper, we study the Compressed Sensing (CS) image recovery problem. The traditional method divides the image into blocks and treats each block as an independent sub-CS recovery task. This often results in losing global structure of an image. In order to improve the CS recovery result, we propose a nonlocal estimation step after the initial CS recovery for de-noising purpose. The nonlocal estimation is based on the well-known nonlocal means (NL) filtering that takes advantage of self-similarity in images. We formulate the nonlocal estimation as the low-rank matrix approximation problem where the low-rank matrix is formed by the nonlocal similarity patches. An efficient algorithm, Extended NonLocal Douglas-Rachford (E-NDLR), based on Douglas-Rachford splitting is developed to solve this low-rank optimization problem constrained by the CS measurements. Experimental results demonstrate that the proposed E-NDLR algorithm achieves significant performance improvements over the state-of-the-art in CS image recovery.