Single image super resolution reconstruction based on genetic algorithm and regularization prior model
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.
Inf. Sci.