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22 October 2024 DBCE-Net: discrete brightness curve estimation for low-light image enhancement
Shize Wang, Jin Wang, Qing Zhu, Yunhui Shi
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Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132740Q (2024) https://doi.org/10.1117/12.3037703
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China
Abstract
The curve-based lighting adjustment technique is widely used in fields such as photography and image processing. The deep learning-based lighting curve adjustment method has shown excellent performance in the field of low-light image enhancement. However, existing curve-based deep learning methods tend to use complex mathematical formulas to define the curve model and add a large number of regularization constraints to ensure that the curve conforms to real physical scenes. This limits the flexibility of the lighting curve, making it unable to accurately enhance brightness for low-light images, resulting in problems such as regional color distortion and overall color bias. To solve this problem, we propose a novel low-light image enhancement model called Discrete Brightness Curve Estimation (DBCE-Net). In DBCE-Net, we introduce a new method for defining curves to enhance regional illumination more effectively. At the same time, we propose a discrete parameter calculation network based on mutual attention mechanism to estimate the discrete brightness adjustment curve from low-light images. Finally, we use a multi-scale denoising network to handle noise introduced by brightness enhancement in shadow areas. Extensive experiments on various datasets have demonstrated that our DBCENet achieves competitive performance in terms of objective quantitative metrics and subjective visual quality evaluation.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Shize Wang, Jin Wang, Qing Zhu, and Yunhui Shi "DBCE-Net: discrete brightness curve estimation for low-light image enhancement", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132740Q (22 October 2024); https://doi.org/10.1117/12.3037703
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KEYWORDS
Image enhancement
Light sources and illumination
Denoising
Feature fusion
Image fusion
Image quality
Image analysis
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