Title :
Optimal incorporation of sparsity information by weighted ℓ1 optimization
Author :
Tanaka, Toshiyuki ; Raymond, Jack
Author_Institution :
Grad. Sch. of Inf., Univ. of Kyoto, Kyoto, Japan
Abstract :
Compressed sensing of sparse sources can be improved by incorporating prior knowledge of the source. In this paper we demonstrate a method for optimal selection of weights in weighted ℓ1 norm minimization for a noiseless reconstruction model, and show the improvements in compression that can be achieved.
Keywords :
information theory; optimisation; compressed sensing; noiseless reconstruction model; norm minimization; optimal incorporation; optimization; sparsity information; Compressed sensing; Image coding; Image processing; Image reconstruction; Informatics; Lifting equipment; Minimization methods; Physics; Random variables; Topology;
Conference_Titel :
Information Theory Proceedings (ISIT), 2010 IEEE International Symposium on
Conference_Location :
Austin, TX
Print_ISBN :
978-1-4244-7890-3
Electronic_ISBN :
978-1-4244-7891-0
DOI :
10.1109/ISIT.2010.5513420
