Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement

Abstract

Video and images acquired by a visual system are seriously degraded under hazy and foggy weather, which will affect the detection, tracking, and recognition of targets. Thus, restoring the true scene from such a foggy video or image is of significance. The main goal of this paper was to summarize current video and image defogging algorithms. We first presented a review of the detection and classification method of a foggy image. Then, we summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogging algorithms. We also presented current video defogging algorithms. We summarized objective image quality assessment methods that have been widely used for the comparison of different defogging algorithms, followed by an experimental comparison of various classical image defogging algorithms. Finally, we presented the problems of video and image defogging which need to be further studied. The code of all algorithms will be available at <;uri xlink:href="http://www.yongxu.org/lunwen.html" xlink:type="simple">http://www.yongxu.org/lunwen.html<;/uri>.

Keywords

• Foggy image classification
• image defogging
• video defogging
• image quality assessment