文章基本信息

标题：Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries
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作者：Liao, Xiuxiu ; Bai, Kejia ; Zhang, Qian 等
期刊名称：COMPUTING AND INFORMATICS
印刷版ISSN：1335-9150
出版年度：2020
卷号：38
期号：6
页码：1301-1319
出版社：COMPUTING AND INFORMATICS
其他摘要：Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures.
其他关键词：Image patch classification; multi-class dictionaries; phase congruency; sparse coding; super-resolution