Image demosaicing using Deep Image Prior

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148723" target="_blank" >RIV/00216305:26220/23:PU148723 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Image demosaicing using Deep Image Prior
Original language description
The paper focuses on the problem of image demosaicing using the deep image prior. The deep image prior (DIP) is an uncommon concept that uses a generative neural network which, however, utilizes only the degraded image as the input for training. A novel method for image demosaicing is proposed, based on DIP, and it is compared with common demosaicing methods. In terms of the objective PSNR and SSIM values, the proposed method proved to be comparable with a widely used Malvar’s demosaicing method. Nevertheless, subjectively, DIP produces demosaiced images comparable with the superior Menon’s algorithm. Unfortunately, the proposed method turned out to be computationally immensely challenging.
Czech name
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Czech description
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Project
<a href="/en/project/GA23-07294S" target="_blank" >GA23-07294S: From perceptron to perception: psychoacoustically motivated audio reconstruction using learned components</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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