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19 October 2022 Three-channel Retinex decomposition for low-light image enhancement
Yangming Shi, Xiaopo Wu, Ming Zhu
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Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122944A (2022) https://doi.org/10.1117/12.2639922
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China
Abstract
Low-Light Image Enhancement (LLIE) is generally regarded as a challenging task that aims to brighten the low-light images and reduce the noise. Many studies have contributed to the community by applying different technologies, including Retinex-based ones. However, most Retinex-based methods dealt with the LLIE issue in mono-channel decomposition way, leading to the imperfect results. In this work, a novel two-stage convolutional neural network (CNN)-based framework composed of the three-channel Retinex decomposition module and the illumination enhancement module is proposed to deal with the LLIE issue. Inspired by the Retinex theory, the former is optimized by introducing the reflectance consistency loss and the well-designed illumination regularization loss, while the latter is supervised by the parameter loss and the enhancement loss. It has proven more effective and flexible, when comparing with the prevalent mono-channel decomposition approaches, due to the capabilities of illumination adjustment, global optimization and shot noise suppression. Both the quantitative and qualitative experiment results on the Low-Light (LOL) dataset demonstrate the better performance of the proposed three-channel Retinex decomposition network in comparison with other works.
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Yangming Shi, Xiaopo Wu, and Ming Zhu "Three-channel Retinex decomposition for low-light image enhancement", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122944A (19 October 2022); https://doi.org/10.1117/12.2639922
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KEYWORDS
Image enhancement
RGB color model
Visualization
Image fusion
Image quality
Image processing
Process modeling
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