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Albluwi, Fatma, Krylov, Vladimir A and Dahyot, Rozenn (2019) Super-Resolution on Degraded Low-Resolution Images Using Convolutional Neural Networks. 2019 27th European Signal Processing Conference (EUSIPCO). pp. 1-5. ISSN 2076-1465
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Abstract
Single Image Super-Resolution (SISR) has witnessed a dramatic improvement in recent years through the use of deep learning and, in particular, convolutional neural networks (CNN). In this work we address reconstruction from low-resolution images and consider as well degrading factors in images such as blurring. To address this challenging problem, we propose a new architecture to tackle blur with the down-sampling of images by extending the DBSRCNN architecture [1]. We validate our new architecture (DBSR) experimentally against several state of the art super-resolution techniques.
| Item Type: | Article |
|---|---|
| Keywords: | Super-Resolution; Degraded; Low-Resolution; Images; Convolutional Neural Networks; |
| Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 15251 |
| Identification Number: | 10.23919/EUSIPCO.2019.8903000 |
| Depositing User: | Rozenn Dahyot |
| Date Deposited: | 17 Jan 2022 12:56 |
| Journal or Publication Title: | 2019 27th European Signal Processing Conference (EUSIPCO) |
| Publisher: | IEEE |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/15251 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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