基于非局部和先验约束的多尺度 图像去雾网络研究.

The image dehazing method based on deep learning has achieved better results than the traditional methods, and has been used widely. In this paper, image prior information is integrated into convolutional neural network, and a multi-scale image dehazing network based on non-local and prior constrain ts is proposed. In this network, a non-local and multi-scale reconstruction module is designed to capture the image s elf-similarity and reconstruction information at different scales. A loss function which contains dark channel prior, mean square error and content difference is proposed. At the same time, a phased optimization method is proposed to effectively improve the training efficiency. Compared with other dehazing methods, the proposed method can achieve better dehazing images. [ABSTRACT FROM AUTHOR]