With the rapid development of artificial intelligence, computer vision systems based on image recognition, object detection, target tracking and other technologies are widely used in aviation, military industry, agriculture and other fields. However, due to bad weather conditions, such as haze and rain, the quality of the images collected by this type of system is impaired, which directly causes its performance to decline, causing serious losses to related fields. Therefore, the research on removing rain and fog on images has attracted the attention of many scholars. In recent years, deep learning has shined in the field of computer vision. Hence, many scholars have combined deep learning methods to remove rain and fog on degraded images and have achieved certain results. In order to gain a deeper understanding of the research progress of the single image rain and fog removal algorithm based on deep learning, this paper collates and analyzes some related literature, introduces and summarizes the algorithm research of the two application scenarios of rain and fog removal in detail. Finally, this paper will briefly summarize these rain and fog removal algorithms and puts forward a prospect for the research of deep learning in single image rain and fog removal.
The image captured in foggy weather is often degraded, traditional defogging algorithms take a long time.Concerning this issue, in this paper, we propose a fast defogging method based on the quickly edge-preserving filtering algorithm, and apply it into image defogging.First, the atmospheric veil is estimated by taking use of the properties that the quickly edge-preserving filtering is available to preserve edge and smooth noise, so as we can solve the atmospheric transmittance distribution. Then, estimating the atmospheric light by quadtree search algorithm .Finally, the haze-free image is recovered by transforming the atmospheric scattering model. The experimental results show that the algorithm can effectively restore the fog image,and has a small time complexity, which is beneficial to the realization of real-time defogging.
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