Abstract
Photographs taken under foggy conditions are prone to low contrast, color deviation, and blurred local details, which are detrimental to advanced computer vision tasks such as image recognition, semantic segmentation, and image detection. Although deep learning techniques have made significant progress in image dehazing, the effectiveness and role of traditional image dehazing methods are still fundamental and have their unique advantages. Based on this, the principles and advantages of the existing advanced traditional methods are analyzed from image restoration and enhancement perspectives, and standard datasets are introduced. Finally, predictions for the future direction of the traditional image dehazing field are presented.
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