Albluwi, Fatma and 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:	https://doi.org/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
URI:	Organisation
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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