A dual sparse and low rank representation for single image super-resolution: A self-learning approach (original) (raw)

2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), 2017

Abstract

Recently, the sparse representations are one of the most active research areas. Here, the problem of single image super-resolution is revisited with sparse and low rank priors. The introduced algorithm employs a self-learning approach. This self-learning approach is applied on cluster domain rather than the common used patch domain. For supporting the self-learning approach, the learning model adopts an incoherence property with the classical sparse priors. In addition, to compensate the weakness of the high frequency details of the underlying low-resolution image, an edge preserving low lark model is proposed. Hence, the low rank representation guarantees the global structure constraints in the recovered high-resolution images. Experimental results, on different datasets, show that the proposed algorithm can recover high-resolution images compared with the state-of-the art.

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