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Volume: 26 | Article ID: art00013
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Deep Residual Network for Joint Demosaicing and Super-Resolution
  DOI :  10.2352/ISSN.2169-2629.2018.26.75  Published OnlineNovember 2018
Abstract

The two classic image restoration tasks, demosaicing and super-resolution, have traditionally always been studied independently. That is sub-optimal as sequential processing, demosaicing and then super-resolution, may lead to amplification of artifacts. In this paper, we show that such accumulation of errors can be easily averted by jointly performing demosaicing and super-resolution. To this end, we propose a deep residual network for learning an end-to-end mapping between Bayer images and high-resolution images. Our deep residual demosaicing and super-resolution network is able to recover high-quality super-resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately. We perform extensive experiments to show that our deep residual network achieves demosaiced and super-resolved images that are superior to the state-of-the-art both qualitatively and quantitatively.

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Ruofan Zhou, Radhakrishna Achanta, Sabine Süsstrunk, "Deep Residual Network for Joint Demosaicing and Super-Resolutionin Proc. IS&T 26th Color and Imaging Conf.,  2018,  pp 75 - 80,  https://doi.org/10.2352/ISSN.2169-2629.2018.26.75

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Copyright © Society for Imaging Science and Technology 2018
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Color and Imaging Conference
color imaging conf
2166-9635
Society for Imaging Science and Technology