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

The phenomenon of image quality degradation in hazy weather has a negative impact on photography work. The contrast of the image will decrease and the color will shift. Meantime, the texture and edge of objects in the scene will become blurred. As shown in Figure 1, there is an obvious difference between the pixel histograms of hazy and haze-free images. For computer vision tasks such as object detection and image segmentation, low-quality inputs can degrade the performance of the models trained on haze-free images. Therefore, many researchers try to recover high-quality clear scenes from hazy images. Before deep learning was widely used in computer vision tasks, image dehazing algorithms had mainly relied on various prior assumptions [51] Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.