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With the development of convolutional neural networks, hundreds of deeplearning based dehazing methods have been proposed. In this paper, we provide acomprehensive survey on supervised, semi-supervised, and unsupervised singleimage dehazing. We first discuss the physical model, datasets, network modules,loss functions, and evaluation metrics that are commonly used. Then, the maincontributions of various dehazing algorithms are categorized and summarized.Further, quantitative and qualitative experiments of various baseline methodsare carried out. Finally, the unsolved issues and challenges that can inspirethe future research are pointed out. A collection of useful dehazing materialsis available at \url{https://github.com/Xiaofeng-life/AwesomeDehazing}.

A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning