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- W2783270057 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." @default.
- W2783270057 created "2018-01-26" @default.
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- W2783270057 date "2017-12-01" @default.
- W2783270057 modified "2023-09-22" @default.
- W2783270057 title "A dual sparse and low rank representation for single image super-resolution: A self-learning approach" @default.
- W2783270057 doi "https://doi.org/10.1109/intelcis.2017.8260027" @default.
- W2783270057 hasPublicationYear "2017" @default.
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