Compressive sensing based imaging via Belief Propagation

Multiple description coding (MDC) using Compressive sensing (CS) mainly aims at restoring the image from a small subset of samples with reasonable accuracy using an iterative message passing decoding algorithm commonly known as Belief Propagation (BP). CS technique can accurately recover any compressible or sparse signal from a lesser number of non-adaptive, randomized linear projection samples than that essential by the Nyquist rate. In this paper, we demonstrate how the BP algorithm reconstructs the image from the measurements generated using the sparse image signal and the measurement matrix. Thus we prove that this algorithm is effective even in the absence of side information. The proposed algorithm exhibits remarkable performance in the reconstruction time as well as reconstruction accuracy.