In order to solve the problems of difficulty in obtaining paired data sets and poor quality of enhanced images in the process of low-light image enhancement,the implementation of unpaired low-light image enhancement was studied by improving the cy-cle generative adversarial network model.In the generator part,a U-NET model integrated with the Vision Transformer structure was used to replace the original generator model,so as to improve the cycle consistency and content retention of image transformation,and effectively deal with the problem of long-distance spatial correlation commonly existing in image research.In the discriminator part,PatchGAN was selected to replace the traditional discriminator according to the characteristics of image research to improve the discrim-ination ability of image details.At the same time,the identity consistency loss function was introduced to improve the image quality.The results show that compared with the traditional method,the improved model in this paper has better subjective visual effect,and al-so has a corresponding improvement in the objective evaluation index.It can be seen that the improved model in this paper is effective.
关键词
深度学习/图像增强/低光图像增强/循环生成对抗网络/Vision/Transformer
Key words
deep learning/image enhancement/low-light image enhancement/cycle generative adversarial network/vision transformer
维普
万方数据