Liu, Chuangchuang, Sun, Xianfang  ORCID: https://orcid.org/0000-0002-6114-0766, Chen, Changyou, Rosin, Paul ORCID: https://orcid.org/0000-0002-4965-3884, Yan, Yitong, Jin, Longcun and Pen, Xinyi
2019.
Multi-scale residual hierarchical dense networks for single image super-resolution.
IEEE Access
7
, 60572 -60583.
10.1109/ACCESS.2019.2915943
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Abstract
Single image super-resolution is known to be an ill-posed problem, which has been studied for decades. With the developments of deep convolutional neural networks, the CNN-based single image super-resolution methods have greatly improved the quality of the generated high-resolution images. However, it is difficult for image super-resolution to make full use of the relationship between pixels in low-resolution images. To address this issue, we propose a novel multi-scale residual hierarchical dense network, which tries to find the dependencies in multi-level and multi-scale features. Specially, we apply the atrous spatial pyramid pooling, which concatenates multiple atrous convolutions with different dilation rates, and design a residual hierarchical dense structure for single image super-resolution. The atrous-spatial pyramid-pooling module is used for learning the relationship of features at multiple scales; while the residual hierarchical dense structure, which consists of several hierarchical dense blocks with skip connections, aims to adaptively detect key information from multi-level features. Meanwhile, dense features from different groups are connected in a dense approach by hierarchical dense blocks, which can adequately extract local multi-level features. Extensive experiments on benchmark datasets illustrate the superiority of our proposed method compared with state-of-the-art methods. The super-resolution results on benchmark datasets of our method can be downloaded from https://github.com/Rainyfish/MS-RHDN, and the source code will be released upon acceptance of the paper.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| ISSN: | 2169-3536 |
| Date of First Compliant Deposit: | 15 May 2019 |
| Date of Acceptance: | 6 May 2019 |
| Last Modified: | 04 Dec 2024 06:15 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/122393 |
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