A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement - Zhang, Jingsi; Wu, Chengdong; Yu, Xiaosheng; Lei, Xiaoliang (Frontiers in Neurorobotics, 2021)
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.