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Single image super-resolution based on residual learning and convolutional sparse coding
Author(s): Chao Xie; Shengqin Jiang; Xiaobo Lu
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Paper Abstract

In this paper, we propose an improved method for single image super-resolution on the basis of residual learning and convolutional sparse coding (CSC). The key idea of it is to first perform a CSC-based decomposition on the input so that it can be split into two predefined parts: the smooth and residual components. Then, the extracted components are individually mapped according to their own characteristics, rather than directly perform a mapping from the original input. Specifically, we place more emphases on the residual one as it is much important to our task, while the smooth one is just propagated to the final output for providing a quick reference. Accordingly, the final architecture of our method conceptually integrates all the above steps into a completely end-to-end trainable deep network. Extensive experimental results indicate that our proposed method outperforms many state-of-the-art methods in terms of both visual fidelity and objective evaluation.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080639 (9 August 2018); doi: 10.1117/12.2503257
Show Author Affiliations
Chao Xie, Southeast Univ. (China)
Ministry of Education (China)
Shengqin Jiang, Southeast Univ. (China)
Ministry of Education (China)
Xiaobo Lu, Southeast Univ. (China)
Ministry of Education (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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