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Authors: Georgy Perevozchikov 1 ; 2 and Egor Ershov 2

Affiliations: 1 Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia ; 2 Institute for Information Transmission Problems, Moscow, Russia

Keyword(s): Computational Photography, Image Signal Processing Pipeline, Domain Adaptation, Image Processing.

Abstract: Nowadays the quality of mobile phone cameras plays one of the most important roles in modern smartphones, as a result, more attention is being paid to the camera Image Signal Processing (ISP) pipeline. The current goal of the scientific community is to develop a neural-based end-to-end pipeline to remove the expensive and exhausting process of classical ISP tuning for each next device. The main drawback of the neural-based approach is the necessity of preparing large-scale datasets each time a new smartphone is designed. In this paper, we address this problem and propose a new method for few-shot domain adaptation of the existing neural ISP to a new domain. We show that it is sufficient to have 10 labeled images of the target domain to achieve state-of-the-art performance on the real camera benchmark datasets. We also provide a comparative analysis of our proposed approach with other existing ISP domain adaptation methods and show that our approach allows us to achieve better results . Our proposed method exhibits notably comparable performance, with only a marginal 2% drop in performance compared to the learned from scratch in the whole dataset baseline. We believe that this solution will significantly reduce the cost of neural-based ISP production for each new device. (More)

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Paper citation in several formats:
Perevozchikov, G. and Ershov, E. (2024). Learning End-to-End Deep Learning Based Image Signal Processing Pipeline Using a Few-Shot Domain Adaptation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 255-263. DOI: 10.5220/0012268900003660

@conference{visapp24,
author={Georgy Perevozchikov. and Egor Ershov.},
title={Learning End-to-End Deep Learning Based Image Signal Processing Pipeline Using a Few-Shot Domain Adaptation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={255-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012268900003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Learning End-to-End Deep Learning Based Image Signal Processing Pipeline Using a Few-Shot Domain Adaptation
SN - 978-989-758-679-8
IS - 2184-4321
AU - Perevozchikov, G.
AU - Ershov, E.
PY - 2024
SP - 255
EP - 263
DO - 10.5220/0012268900003660
PB - SciTePress

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