The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. However, there are few studies that summarize the deep learning (DL) based dehazing technologies. In this paper, we conduct a comprehensive survey on the recent proposed dehazing methods. Firstly, we summarize the commonly used datasets, loss functions and evaluation metrics. Secondly, we group the existing researches of ID into two major categories: supervised ID and unsupervised ID. The core ideas of various influential dehazing models are introduced. Finally, the open issues for future research on ID are pointed out.
翻译：烟雾的存在大大降低了图像的质量。研究人员设计了各种图像脱色算法(ID),以恢复烟雾图像的质量。然而,很少有研究总结了以深层学习(DL)为基础的脱色技术。在本文件中,我们对最近提出的脱色方法进行了全面调查。首先,我们总结了常用的数据集、损失功能和评估指标。第二,我们将现有的身份识别研究分为两大类:受监督的ID和不受监督的ID。介绍了各种有影响力的脱色模型的核心想法。最后,我们指出今后关于ID研究的公开问题。
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