0575759 20240402214439.520230922235959.9 10.1109/ICIP49359.2023.10221948 5 s. E cav_un_epca*0575755978-1-7281-9835-41735-1739PiscatawayIEEE2023 Demosaicking neural field implicit neural representation cav_un_auth*0379363 Kerepecký Tomáš UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101209 Šroubek Filip UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0283562 Novozámský Adam UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101087 Flusser Jan UTIA-B Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2023/ZOI/kerepecky-0575759.pdf GA21-03921S GA ČR cav_un_auth*0412209 StrategieAV21/1 AV ČR CZ cav_un_auth*0441412 We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns. Our approach leverages advancements in neural fields to perform demosaicking by representing an image as a coordinate-based neural network with sine activation functions. The inputs to the network are spatial coordinates and a low-resolution Bayer pattern, while the outputs are the corresponding RGB values. An encoder network, which is a blend of ResNet and U-net, enhances the implicit neural representation of the image to improve its quality and ensure spatial consistency through prior learning. Our experimental results demonstrate that NeRD outperforms traditional and state-of-the-art CNN-based methods and significantly closes the gap to transformer-based methods. cav_un_auth*0455338 IEEE International Conference on Image Processing 2023 (ICIP 2023) 20231008 20231011 Kuala Lumpur MY JC 10000 10200 10201 2024 4 PR RVO:67985556 https://hdl.handle.net/11104/0345842 cav_un_auth*0329918 FJFI ČVUT Praha CZ S 2023 SCOPUS PUBMED WOS cav_un_epca*0575755 Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP) 978-1-7281-9835-4 1735 1739 Piscataway IEEE 2023