Avoiding Undesirable Solutions of Deep Blind Image Deconvolution

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Avoiding Undesirable Solutions of Deep Blind Image Deconvolution

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Avoiding Undesirable Solutions of Deep Blind Image Deconvolution

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

Počet stran výsledku

7

Strana od-do

559-566

Název nakladatele

SciTePress

Místo vydání

Setúbal

Místo konání akce

Roma

Datum konání akce

27. 2. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—