Craven, Stephen D.; Yaqub, Raziq
College of Engineering and Computer Science
University of Tennessee at Chattanooga
Place of Publication
Multiple description coding (MDC) using Compressive Sensing (CS) mainly aims at restoring an image from a small subset of samples with reasonable accuracy using an iterative message passing decoding algorithm commonly known as Belief Propagation (BP). The CS technique can accurately recover any compressible or sparse signal from a lesser number of non-adaptive, randomized linear projection samples than that specified by the Nyquist rate. In this work, we demonstrate how CS-based encoding generates measurements from the sparse image signal and the measurement matrix. Then we demonstrate how a BP decoding algorithm reconstructs the image from the measurements generated. In our work, the CS-BP algorithm assumes that all the unknown variables have the same prior distribution as we do not have any knowledge of the side information available during the initiation of the decoding process. Thus, we prove that this algorithm is effective even in the absence of side information.
M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.
Computer algorithms; Data structures (Computer science); Signal processing -- Digital techniques
Electrical and Computer Engineering
xi; 25 leaves
Ramachandra, Preethi Modur, "Compressive sensing based imaging via belief propagation" (2012). Masters Theses and Doctoral Dissertations.