With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised 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.
翻译:由于发展了革命性神经网络,提出了数百种基于深层学习的脱轨方法,本文件对受监督、半监督和无人监督的脱轨方法进行了全面调查,首先讨论了通常使用的物理模型、数据集、网络模块、损失功能和评价指标,然后对各种脱轨算法的主要贡献进行了分类和总结,还进行了各种基线方法的定量和定性实验,最后,指出了能够激发未来研究的未决问题和挑战,在https://github.com/Xiaofeng-lif/Awesoph Dehazing上提供了有用的脱轨材料汇编。