Compressive sensing based imaging via belief propagation

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.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Ramachandra, Preethi Modur

Contributors dc:contributor

• Sartipi, Mina
• Craven, Stephen D.; Yaqub, Raziq
• College of Engineering and Computer Science

• Computer algorithms
• Data structures (Computer science)
• Signal processing -- Digital techniques

Licence

• https://rightsstatements.org/page/InC/1.0/?language=en

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/62

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1064