Avoiding Undesirable Solutions of Deep Blind Image Deconvolution
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00583748" target="_blank" >RIV/67985556:_____/24:00583748 - isvavai.cz</a>
Result on the web
<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0012397600003660" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0012397600003660</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0012397600003660" target="_blank" >10.5220/0012397600003660</a>
Alternative languages
Result language
angličtina
Original language name
Avoiding Undesirable Solutions of Deep Blind Image Deconvolution
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024)
ISBN
978-989-758-679-8
ISSN
2184-4321
e-ISSN
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Number of pages
7
Pages from-to
559-566
Publisher name
SciTePress
Place of publication
Setúbal
Event location
Roma
Event date
Feb 27, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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