A summary of research progress of single image to remove rain and fog based on deep learning

Xiaoqi Qin; Xiaoqin Liu; Chao Mu; Kang Mei

doi:10.1117/12.2586305

12 March 2021 A summary of research progress of single image to remove rain and fog based on deep learning

Xiaoqi Qin, Xiaoqin Liu, Chao Mu, Kang Mei

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 117631T (2021) https://doi.org/10.1117/12.2586305
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

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.

Citation Download Citation

Xiaoqi Qin, Xiaoqin Liu, Chao Mu, and Kang Mei "A summary of research progress of single image to remove rain and fog based on deep learning", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 117631T (12 March 2021); https://doi.org/10.1117/12.2586305

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