Learning optimum binary color filter arrays for demosaicing with neural networks

Cemre Omer Ayna; Ali Cafer Gurbuz

doi:10.1117/12.3013594

7 June 2024 Learning optimum binary color filter arrays for demosaicing with neural networks

Cemre Omer Ayna, Ali Cafer Gurbuz

Proceedings Volume 13034, Real-Time Image Processing and Deep Learning 2024; 130340M (2024) https://doi.org/10.1117/12.3013594
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

Modern digital color cameras depend on Color Filter Arrays (CFA) for capturing color information. The majority of the commercial CFAs are designed by hand with different physical and application-specific considerations. The available machine learning (ML)-based CFA learning architectures dismiss the considerations of a physical camera device. This study aims to develop an alternative approach to jointly learn binary Color Filter Arrays (CFA) in a deep learning-based filtering-demosaicing pipeline. The proposed approach provides higher reconstruction performance than the compared hand-designed filters while learning physically applicable CFAs. This paper includes the learned binary CFAs for various color configurations and training data size, their analysis with common reconstruction metrics, and a short discussion on future works.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cemre Omer Ayna and Ali Cafer Gurbuz "Learning optimum binary color filter arrays for demosaicing with neural networks", Proc. SPIE 13034, Real-Time Image Processing and Deep Learning 2024, 130340M (7 June 2024); https://doi.org/10.1117/12.3013594

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