The image collected in low-light environment has the characteristic with high noise and color distortion, which makes it difficult to utilize the image and can not fully explore the rich value information of the image. In order to improve the quality of low-light image, this paper proposes a Heterogenous low-light image enhancement method based on DenseNet generative adversarial network. Firstly, the generative network of generative adversarial network is realized by using DenseNet framework. Secondly, the feature map from low illumination image to normal illumination image is learned by using the generative adversarial network. Thirdly, the enhancement of low-light image is realized. The experimental results show that, compared with the state-of-the-art enhancement algorithms, the proposed method can improve the image brightness more effectively and reduce the noise of enhanced image.

中文翻译：

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一种新型的DenseNet生成对抗网络，用于异构低光图像增强

在弱光环境下采集的图像具有噪声高，色彩失真大的特点，难以利用，无法充分发掘图像的丰富价值信息。为了提高弱光图像的质量，提出了一种基于DenseNet生成对抗网络的异构弱光图像增强方法。首先，利用DenseNet框架实现了生成对抗网络的生成网络。其次，利用生成对抗网络学习从低照度图像到正常照度图像的特征图。第三，实现了弱光图像的增强。实验结果表明，与最新的增强算法相比，