Affiliations: [a] College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin, China
| [b] Xingtai Key Laboratory for Research and Application of Robot Intelligent Detection and Sorting Technology, Xingtai University, Xingtai, HeBei, China
| [c] College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin, China
| [d] School of Physics and Electrical Engineering, Xingtai University, Xingtai, HeBei, China
Correspondence:
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Corresponding author. Xiaoyan Chen, College of Electronic Information and Automation, Tianjin University of Science and Technology, 1038 Dagu South Road, Tianjin, 300222, China. E-mail: [email protected].
Abstract: This paper proposes that the task of single-image low-light enhancement can be accomplished by a straightforward method named Opt2Ada. It contains a series of pixel-level operations, including an optimized illuminance channel decomposition, an adaptive illumination enhancement, and an adaptive global scaling. Opt2Ada is traditional and it does not rely on architecture engineering, super-parameter tuning, or specific training dataset. Its parameters are generic and it has better generalization capability than existing data-driven methods. For evaluation, both the full-reference, non-reference, and semantic metrics are calculated. Extensive experiments on real-world low-light images demonstrate the superiority of Opt2Ada over recent traditional and deep learning algorithms. Due to its flexibility and effectiveness, Opt2Ada can be deployed as a pre-processing subroutine for high-level computer vision applications.
Keywords: Low-light image enhancement, Image processing, Traditional method
