期刊
INFORMATION SCIENCES
卷 372, 期 -, 页码 196-207出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.08.049
关键词
Single image super-resolution; Genetic algorithm; Regularization prior model; Non-local means
资金
- National Natural Science Foundation of China [61272279, 61272282, 61371201, 61203303]
- Program for New Century Excellent Talents in University [NCET-12-0920]
- National Basic Research Program (973 Program) of China [2013CB329402]
- Program for Cheung Kong Scholars and Innovative Research Team in University [IRT_15R53]
- Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) [B07048]
Single image super-resolution (SR) reconstruction is an ill-posed inverse problem because the high-resolution (HR) image, obtained from the low-resolution (LR) image, is non unique or unstable. In this paper, single image SR reconstruction is treated as an optimization problem, and a new single image SR method, based on a genetic algorithm and regularization prior model, is proposed. In the proposed method, the optimization problem is constructed with a regularization prior model which consists of the non-local means (NLMs) filter, total variation (TV) and adaptive sparse domain selection (ASDS) scheme for sparse representation. In order to avoid local optimization, we combine the genetic algorithm and the iterative shrinkage algorithm to deal with the regularization prior model. Compared with several other state-of-the-art algorithms, the proposed method demonstrates better performances in terms of both numerical analysis and visual effect. (C) 2016 Elsevier Inc. All rights reserved.
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