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Authors: Siavash Bigdeli 1 ; David Honzátko 1 ; Sabine Süsstrunk 2 and L. Andrea Dunbar 1

Affiliations: 1 Centre Suisse d’Electronique et de Microtechnique (CSEM), Neuchâtel, Switzerland ; 2 School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

Keyword(s): Image Restoration, Image Denoising, MAP, Neural Networks, Deep Learning.

Abstract: Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP denoising optimization. We present the first end-to-end approach to MAP estimation for image denoising using deep neural networks. We show that our method is guaranteed to minimize the MAP denoising objective, which is then used in an optimization algorithm for generic image restoration. We provide theoretical analysis of our approach and show the quantitative performance of our method in several experiments. Our experimental results show that the proposed method can achieve 70x faster performance compared to the state-of-the-art, while maintaining the theoretical perspective of MAP.

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Paper citation in several formats:
Bigdeli, S.; Honzátko, D.; Süsstrunk, S. and Dunbar, L. (2020). Image Restoration using Plug-and-Play CNN MAP Denoisers. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 85-92. DOI: 10.5220/0008990700850092

@conference{visapp20,
author={Siavash Bigdeli. and David Honzátko. and Sabine Süsstrunk. and L. Andrea Dunbar.},
title={Image Restoration using Plug-and-Play CNN MAP Denoisers},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={85-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008990700850092},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Image Restoration using Plug-and-Play CNN MAP Denoisers
SN - 978-989-758-402-2
IS - 2184-4321
AU - Bigdeli, S.
AU - Honzátko, D.
AU - Süsstrunk, S.
AU - Dunbar, L.
PY - 2020
SP - 85
EP - 92
DO - 10.5220/0008990700850092
PB - SciTePress

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