Abstract
While deep learning-based single image super-resolution has progressed significantly, super-resolution of images containing blurring artifacts is still challenging. This paper proposes a unified model that can simultaneously perform deblurring and super-resolution for a given image, which is called Edge Attention Network (EAN). Our model employs an attention mechanism using the edge information in order to enhance the sharpness of the super-resolved output image. Experimental results demonstrate that our method outperforms existing super-resolution methods and separate application of deblurring and super-resolution.
| Original language | English |
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| Title of host publication | 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2401-2406 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665442077 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne, Australia Duration: 2021 Oct 17 → 2021 Oct 20 |
Publication series
| Name | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
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| ISSN (Print) | 1062-922X |
Conference
| Conference | 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 |
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| Country/Territory | Australia |
| City | Melbourne |
| Period | 21/10/17 → 21/10/20 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction