Single image super-resolution reconstruction based on genetic algorithm and regularization prior model

Yangyang Li, Yang Wang, Yaxiao Li, Licheng Jiao, Xiangrong Zhang, Rustam Stolkin

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
268 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)196-207
JournalInformation Sciences
Volume372
Early online date16 Aug 2016
DOIs
Publication statusPublished - 1 Dec 2016

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