Open AccessInfrared image super-resolution via locality-constrained group sparse model
Author(s) -
Chengzhi Deng,
Wei Tian,
Ci Pan,
Shengqian Wang,
Haihong Zhu,
Saifeng Hu
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.044202
Subject(s) - locality , computer science , artificial intelligence , resolution (logic) , infrared , sparse approximation , group (periodic table) , image (mathematics) , pattern recognition (psychology) , singular value decomposition , low resolution , similarity (geometry) , manifold (fluid mechanics) , computer vision , algorithm , high resolution , physics , optics , remote sensing , philosophy , linguistics , quantum mechanics , mechanical engineering , engineering , geology
Aiming at the problems of low-resolution and poor visual quality of infrared images, a locality-constrained group sparsity based infrared image super-resolution algorithm is proposed. Firstly with considering the texture self-similarity of infrared images and group structural sparsity of atom coefficients, a locality-constrained group sparse (LCGS) model is proposed. Secondly, under LCGS and K-singular value decomposition, a pair of group structural dictionaries is learned. The dictionary pair can well capture and preserve the intrinsic geometrical manifold of low and high resolution data. Finally, the high-resolution infrared images are recovered by the high-resolution dictionary and the corresponding low-resolution group sparse coefficients. Experimental results show that the proposed method obtains excellent performance in objective evaluation and subjective visual effect.