Research On Video Defogging Algorithm Based On Superpixels Segmentation And Salient Object Detection

In recent years,with the development of video technology,people have increasingly high requirements for video quality.However,due to the frequent occurrence of bad weather,objects in captured videos are blurred,distorted,or become gray.Among them,haze weather is the most common type of life,which seriously affects people’s normal lives.Therefore,how to quickly and effectively remove fog from video is very important.Traditional video defogging methods are mainly based on single frame and multi frame video defogging,but the video recovered by these methods will produce flickering artifacts;With the continuous development of deep learning in video processing,various neural networks have been introduced to solve the problem of video defogging.However,there are still several problems:(1)How to make the recovered single frame fog free image more realistic.(2)How to better utilize spatial and temporal information in consecutive adjacent frames.To solve the above problems,this paper proposes a video defogging algorithm based on superpixels segmentation and a video defogging algorithm based on salient object detection.The main work is as follows:(1)To address the problem of how to align consecutive adjacent frames when aggregating information across multiple frames,and how to use the information of adjacent frames to estimate the transmission map of the input video.In this paper,we propose a video defogging algorithm based on superpixels segmentation,which is added to the network to make full use of the rich information existing between adjacent frames and to estimate the transmission map more accurately,which makes the generated fog-free video more realistic,and the experimental results show that the algorithm has obvious effects for video defogging.(2)For the problem that the recovered fog-free video has patches in the superpixels segmentation-based video defogging algorithm and the problem that the estimated transmission map has unnecessary texture details,a video defogging algorithm based on salient object detection is proposed.The salient object detection algorithm is more concerned with edge details,and combined with the representation of the transmission map in the ideal state,this chapter uses a deep learning model based on salient object detection to estimate the transmission map,and shows through experiments that the algorithm not only has a significant contrast in the de-fogging effect,but also has a great improvement in time efficiency.(3)For the two video defogging algorithms proposed in this paper,an intelligent video defogging system is designed and implemented,after testing the designed system can quickly recover fog-free video,and the recovered video has clearer boundaries,higher visual quality and less color distortion compared to other algorithms.