Implicit neural representation for image demosaicking

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00616904" target="_blank" >RIV/67985556:_____/25:00616904 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/25:00390510
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1051200425000442?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1051200425000442?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.dsp.2025.105022" target="_blank" >10.1016/j.dsp.2025.105022</a>

Result language
angličtina
Original language name
Implicit neural representation for image demosaicking
Original language description
We propose a novel approach to enhance image demosaicking algorithms using implicit neural representations (INR). Our method employs a multi-layer perceptron to encode RGB images, combining original Bayer measurements with an initial estimate from existing demosaicking methods to achieve superior reconstructions. A key innovation is the integration of two loss functions: a Bayer loss for fidelity to sensor data and a complementary loss that regularizes reconstruction using interpolated data from the initial estimate. This combination, along with INR’s inherent ability to capture fine details, enables hig-fidelity reconstructions that incorporate information from both sources. Furthermore, we demonstrate that INR can effectively correct artifacts in state-of-the-art demosaicking methods when input data diverge from the training distribution, such as in cases of noise or blur. This adaptability highlights the transformative potential of INR-based demosaicking, offering a robust solution to this challenging problem.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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)

Project
<a href="/en/project/GA25-15933S" target="_blank" >GA25-15933S: Dynamic Inverse Problems in Time-Lapse Microscopy</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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