颜色恒定性
人工智能
计算机科学
计算机视觉
模式识别(心理学)
先验概率
降噪
人工神经网络
图像(数学)
监督学习
贝叶斯概率
作者
Jinxiu Liang,Yong Xu,Yuhui Quan,Boxin Shi,Hui Gao
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2022-11-01
卷期号:32 (11): 7332-7345
被引量:12
标识
DOI:10.1109/tcsvt.2022.3181781
摘要
This paper proposes a deep learning method for low-light image enhancement, which exploits the generation capability of Neural Networks (NNs) while requiring no training samples except the input image itself. Based on the Retinex decomposition model, the reflectance and illumination of a low-light image are parameterized by two untrained NNs. The ambiguity between the two layers is resolved by the discrepancy between the two NNs in terms of architecture and capacity, while the complex noise with spatially-varying characteristics is handled by an illumination-adaptive self-supervised denoising module. The enhancement is done by jointly optimizing the Retinex decomposition and the illumination adjustment. Extensive experiments show that the proposed method not only outperforms existing non-learning-based and unsupervised-learning-based methods, but also competes favorably with some supervised-learning-based methods in extreme low-light conditions.
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