A Bio-inspired Dark Adaptation Framework for Low-light Image Enhancement

Lei, Fang (2022) A Bio-inspired Dark Adaptation Framework for Low-light Image Enhancement. In: IEEE WCCI 2022-IJCNN regular session, 18-23 July 2022, Padua, Italy.

Full content URL: https://doi.org/10.1109/IJCNN55064.2022.9892877

A Bio-inspired Dark Adaptation Framework for Low-light Image Enhancement
2022089132.pdf - Whole Document

Item Type:Conference or Workshop contribution (Poster)
Item Status:Live Archive


In low light conditions, image enhancement is critical for vision-based artificial systems since details of objects in
dark regions are buried. Moreover, enhancing the low-light image without introducing too many irrelevant artifacts is important for visual tasks like motion detection. However, conventional methods always have the risk of “bad” enhancement. Nocturnal insects show remarkable visual abilities at night time, and their adaptations in light responses provide inspiration for low-light image enhancement. In this paper, we aim to adopt the neural mechanism of dark adaptation for adaptively raising intensities whilst preserving the naturalness. We propose a framework for
enhancing low-light images by implementing the dark adaptation operation with proper adaptation parameters in R, G and B channels separately. Specifically, the dark adaptation in this paper consists of a series of canonical neural computations, including the power law adaptation, divisive normalization and adaptive rescaling operations. Experiments show that the proposed bioinspired dark adaptation framework is more efficient and can better preserve the naturalness of the image compared to existing methods.

Keywords:Bio-inspired dark adaptation, low-light image enhancement, adaptive intensity transformation, canonical neural computations
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:52795
Deposited On:20 Dec 2022 14:31

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