鉴别器
计算机科学
人工智能
噪音(视频)
降噪
计算机视觉
还原(数学)
卷积(计算机科学)
发电机(电路理论)
图像(数学)
模式识别(心理学)
数学
功率(物理)
物理
电信
人工神经网络
量子力学
探测器
几何学
作者
Tian Ma,Chenhui Fu,Ming Guo,Jiayi Yang,Jia Liu
标识
DOI:10.1109/icspcc55723.2022.9984400
摘要
Images taken in low-light conditions would have insufficient light intensity and high noise. Many existing methods could not work very well in low-light environments, such as the noise and artifacts in dark conditions will be more obvious when enhanced. Therefore, low-light image enhancement is a challenging task in computer vision. To solve this problem, this paper proposes a lightweight generative adversarial network with dual-attention units to enhance underexposed photos. There is only a simple two-layer convolution in the generator section, and a dual-attention unit is added between the two convolutions to suppress the noise generated during the enhancement process and the deviation of color reduction. Then, non-local correlations of the image are used in the spatial attention module for denoising. Ours low-light image enhancement network is guided by the channel attention module to optimize redundant color features. In addition, the ideas of PatchGAN and Relativistic GAN are combined in the discriminator section to make the discriminator a better measure of the probability of changing from absolute true or false to relative true or false. The experiment results show that, our method could get better enhancement effects on low-illumination image datasets, which has more natural color, better exposure, and less noise and artifacts.
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