Open AccessSubgradient projection for sparse signal recovery with sparse noise
Author(s) -
Sun Tao,
Zhang Hui,
Cheng Lizhi
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2014.1335
Subject(s) - inpainting , subgradient method , thresholding , noise (video) , artificial intelligence , computer science , projection (relational algebra) , compressed sensing , face (sociological concept) , set (abstract data type) , signal (programming language) , sparse approximation , image (mathematics) , pattern recognition (psychology) , signal to noise ratio (imaging) , sparse matrix , computer vision , algorithm , machine learning , telecommunications , social science , physics , quantum mechanics , sociology , gaussian , programming language
Recovering sparse signals from a few linear measurements with sparse noise is attracting growing attention. Such a problem has appeared in a diverse set of fields such as super‐resolution, image inpainting and face recognition. A new model for this problem on learning the sparsity of the signal is presented. A corresponding algorithm is also presented by combining the subgradient method and the hard thresholding pursuit strategy. Numerical results demonstrate the efficiency of the model and algorithm.