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Authors: Antonie Brožová 1 ; 2 and Václav Šmídl 2

Affiliations: 1 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Czech Republic ; 2 Institute of Information Theory and Automation, Czech Academy of Sciences, Czech Republic

Keyword(s): Blind Image Deconvolution, Deep Image Prior, No-Blur, Variational Bayes.

Abstract: Blind image deconvolution (BID) is a severely ill-posed optimization problem requiring additional information, typically in the form of regularization. Deep image prior (DIP) promises to model a naturally looking image due to a well-chosen structure of a neural network. The use of DIP in BID results in a significant perfor-mance improvement in terms of average PSNR. In this contribution, we offer qualitative analysis of selected DIP-based methods w.r.t. two types of undesired solutions: blurred image (no-blur) and a visually corrupted image (solution with artifacts). We perform a sensitivity study showing which aspects of the DIP-based algorithms help to avoid which undesired mode. We confirm that the no-blur can be avoided using either sharp image prior or tuning of the hyperparameters of the optimizer. The artifact solution is a harder problem since variations that suppress the artifacts often suppress good solutions as well. Switching to the structural similarity index measure fro m L 2 norm in loss was found to be the most successful approach to mitigate the artifacts. (More)

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Paper citation in several formats:
Brožová, A. and Šmídl, V. (2024). Avoiding Undesirable Solutions of Deep Blind Image Deconvolution. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 559-566. DOI: 10.5220/0012397600003660

@conference{visapp24,
author={Antonie Brožová. and Václav Šmídl.},
title={Avoiding Undesirable Solutions of Deep Blind Image Deconvolution},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={559-566},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012397600003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Avoiding Undesirable Solutions of Deep Blind Image Deconvolution
SN - 978-989-758-679-8
IS - 2184-4321
AU - Brožová, A.
AU - Šmídl, V.
PY - 2024
SP - 559
EP - 566
DO - 10.5220/0012397600003660
PB - SciTePress

- Science and Technology Publications, Lda.
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