Learning End-to-End Deep Learning Based Image Signal Processing Pipeline Using a Few-Shot Domain Adaptation

Georgy Perevozchikov, Georgy Perevozchikov, Egor Ershov

2024

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 255-263. DOI: 10.5220/0012268900003660

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Perevozchikov G.
AU - Ershov E.
PY - 2024
SP - 255
EP - 263
DO - 10.5220/0012268900003660
PB - SciTePress