Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation

• URL: http://arxiv.org/abs/2210.03792v1
• Date: Fri, 7 Oct 2022 19:32:55 GMT
• Title: Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation
• Authors: Wenjing Wang, Zhengbo Xu, Haofeng Huang, Jiaying Liu
• Abstract summary: We propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework.
• Score: 36.050270650417325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light enhancement or domain adaptive machine analytics, the former considers less on high-level vision, while the latter neglects the potential of image-level signal adjustment. How to restore underexposed images/videos from the perspective of machine vision has long been overlooked. In this paper, we are the first to propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve, and propose to satisfy this concavity through discrete integral. With the intention of adapting illumination from the perspective of machine vision without task-specific annotated data, we design an asymmetric cross-domain self-supervised training strategy. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework. Comprehensive experiments demonstrate that our method surpasses existing low-light enhancement and adaptation methods and shows superior generalization on various low-light vision tasks, including classification, detection, action recognition, and optical flow estimation. Project website: https://daooshee.github.io/SACC-Website/

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