Few-Shot Domain Adaptation for Low Light RAW Image Enhancement

Prabhakar, KR and Vinod, V and Sahoo, NR and Babu, RV (2021) Few-Shot Domain Adaptation for Low Light RAW Image Enhancement. In: UNSPECIFIED.

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Abstract

Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination. Despite the success of existing Convolutional Neural Network (CNN) based methods, their performance is not adaptable to different camera domains. In addition, such methods also require large datasets with short-exposure and corresponding long-exposure ground truth raw images for each camera domain, which is tedious to compile. To address this issue, we present a novel few-shot domain adaptation method to utilize the existing source camera labeled data with few labeled samples from the target camera to improve the target domain's enhancement quality in extreme low-light imaging. Our experiments show that only ten or fewer labeled samples from the target camera domain are sufficient to achieve similar or better enhancement performance than training a model with a large labeled target camera dataset. To support research in this direction, we also present a new low-light raw image dataset captured with a Nikon camera, comprising short-exposure and their corresponding long-exposure ground truth images. The code is available at https://val.cds.iisc.ac.in/HDR/BMVC21/index.html. © 2021. The copyright of this document resides with its authors.

Item Type:	Conference Paper
Publication:	32nd British Machine Vision Conference, BMVC 2021
Publisher:	British Machine Vision Association, BMVA
Additional Information:	The copyright for this article belongs to British Machine Vision Association, BMVA.
Keywords:	Computer vision; Convolutional neural networks; Image enhancement; Large dataset, Color distortions; Convolutional neural network; Domain adaptation; Exposure-time; Ground truth; Long exposures; Low light; Noise distortions; Performance; Raw images, Cameras
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	04 Mar 2024 09:51
Last Modified:	04 Mar 2024 09:51
URI:	https://eprints.iisc.ac.in/id/eprint/84414

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