Open AccessCompressive sensing via reweighted TV and nonlocal sparsity regularisation
Author(s) -
Dong W.,
Yang X.,
Shi G.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2012.2536
Subject(s) - compressed sensing , piecewise , redundancy (engineering) , maximum a posteriori estimation , computer science , iterative reconstruction , algorithm , a priori and a posteriori , artificial intelligence , mathematical optimization , computer vision , mathematics , maximum likelihood , statistics , mathematical analysis , philosophy , epistemology , operating system
Total variation (TV) regularisation has been widely used for compressive sensing (CS) reconstruction. However, since TV regularisers favour piecewise constant solutions, they tend to produce over‐smoothed image edges. To overcome this drawback, proposed is a novel iteratively reweighted TV regulariser for CS reconstruction. Spatially adaptive weights are computed towards a maximum a posteriori estimation of the image gradients. To exploit the nonlocal redundancy, effective nonlocal sparsity regularisation has also been introduced into the proposed objective function. Experimental results demonstrate that the proposed CS reconstruction method outperforms significantly existing TV‐based CS reconstruction methods.