A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning
Jie Gui, Xiaofeng Cong, Yuan Cao, Wenqi Ren, Jun Zhang, Jing Zhang, Jiuxin Cao, Dacheng Tao
Abstract
With the development of convolutional neural networks, hundreds of deep learning–based dehazing methods have been proposed. In this article, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used. Then, the main contributions of various dehazing algorithms are categorized and summarized. Further, quantitative and qualitative experiments of various baseline methods are carried out. Finally, the unsolved issues and challenges that can inspire the future research are pointed out. A collection of useful dehazing materials is available at https://github.com/Xiaofeng-life/AwesomeDehazing .