Fatma, Albluwi and Krylov, Vladimir A and Dahyot, Rozenn (2018) Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks. Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing. ISSN 1551-2541

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Abstract

Recently multiple high performance algorithms have been developed to infer high-resolution images from low-resolution image input using deep learning algorithms. The related problem of super-resolution from blurred or corrupted low-resolution images has however received much less attention. In this work, we propose a new deep learning approach that simultaneously addresses deblurring and super-resolution from blurred low resolution images. We evaluate the state-of-the-art super-resolution convolutional neural network (SR-CNN) architecture proposed in [1] for the blurred reconstruction scenario and propose a revised deeper architecture that proves its superiority experimentally both when the levels of blur are known and unknown a priori.

Item Type:	Article
Keywords:	Image super-resolution; deblurring; deep learning; convolutional neural networks;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15254
Identification Number:	https://doi.org/10.1109/MLSP.2018.8516983
Depositing User:	Rozenn Dahyot
Date Deposited:	17 Jan 2022 16:57
Journal or Publication Title:	Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
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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