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

: 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.