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Learning to Adapt to Light

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A Correction to this article was published on 13 February 2023

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Abstract

Light adaptation or brightness correction is a key step in improving the contrast and visual appeal of an image. There are multiple light-related tasks (for example, low-light enhancement and exposure correction) and previous studies have mainly investigated these tasks individually. It is interesting to consider whether the common light adaptation sub-problem in these light-related tasks can be executed by a unified model, especially considering that our visual system adapts to external light in such way. In this study, we propose a biologically inspired method to handle light-related image enhancement tasks with a unified network (called LA-Net). First, we proposed a new goal-oriented task decomposition perspective to solve general image enhancement problems, and specifically decouple light adaptation from multiple light-related tasks with frequency-based decomposition. Then, a unified module is built inspired by biological visual adaptation to achieve light adaptation in the low-frequency pathway. Combined with the proper noise suppression and detail enhancement along the high-frequency pathway, the proposed network performs unified light adaptation across various scenes. Extensive experiments on three tasks—low-light enhancement, exposure correction, and tone mapping—demonstrate that the proposed method obtains reasonable performance simultaneously for all of these three tasks compared with recent methods designed for these individual tasks. Our code is made publicly available at https://github.com/kaifuyang/LA-Net.

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Acknowledgements

This study was supported by STI2030-Major Projects (2022ZD0204600) and the National Natural Science Foundation of China (62076055). This work was also partly supported by the Fundamental Research Funds for the Central Universities (ZYGX2019J114).

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  1. Kai-Fu Yang and Cheng Cheng contributed equally to this work.

Authors and Affiliations

  1. MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China

    Kai-Fu Yang, Cheng Cheng, Shi-Xuan Zhao, Hong-Mei Yan, Xian-Shi Zhang & Yong-Jie Li

  2. The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313001, Zhejiang, China

    Kai-Fu Yang, Hong-Mei Yan, Xian-Shi Zhang & Yong-Jie Li

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  1. Kai-Fu Yang
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Yang, KF., Cheng, C., Zhao, SX. et al. Learning to Adapt to Light. Int J Comput Vis 131, 1022–1041 (2023). https://doi.org/10.1007/s11263-022-01745-y

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