Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging

2015 ◽  
Vol 24 (11) ◽  
pp. 3888-3901 ◽  
Author(s):  
Lark Kwon Choi ◽  
Jaehee You ◽  
Alan Conrad Bovik
Keyword(s):  
2011 ◽  
Vol 121-126 ◽  
pp. 887-891
Author(s):  
Bin Xie ◽  
Fan Guo ◽  
Zi Xing Cai

In this paper, we propose a new defog algorithm based on fog veil subtraction to remove fog from a single image. The proposed algorithm first estimates the illumination component of the image by applying smoothing to the degraded image, and then obtains the uniform distributed fog veil through a mean calculation of the illumination component. Next, we multiply the uniform veil by the original image to obtain a depth-like map and extract its intensity component to produce a fog veil whose distribution is according with real fog density of the scene. Once the fog veil is calculated, the reflectance map can be obtained by subtracting the veil from the degraded image. Finally, we apply an adaptive contrast stretching to the reflectance map to obtain an enhanced result. This algorithm can be easily extended to video domains and is verified by both real-scene photographs and videos.


2018 ◽  
Vol 20 (7) ◽  
pp. 1699-1711 ◽  
Author(s):  
Zhigang Ling ◽  
Jianwei Gong ◽  
Guoliang Fan ◽  
Xiao Lu

2017 ◽  
Vol 26 (7) ◽  
pp. 3397-3409 ◽  
Author(s):  
Yutong Jiang ◽  
Changming Sun ◽  
Yu Zhao ◽  
Li Yang

2018 ◽  
Vol 30 (10) ◽  
pp. 1925
Author(s):  
Xumin Cao ◽  
Chunxiao Liu ◽  
Jindong Zhang ◽  
Yuhang Lin ◽  
Jinwei Zhao

Author(s):  
Tannistha Pal

Images captured in severe atmospheric catastrophe especially in fog critically degrade the quality of an image and thereby reduces the visibility of an image which in turn affects several computer vision applications like visual surveillance detection, intelligent vehicles, remote sensing, etc. Thus acquiring clear vision is the prime requirement of any image. In the last few years, many approaches have been made towards solving this problem. In this article, a comparative analysis has been made on different existing image defogging algorithms and then a technique has been proposed for image defogging based on dark channel prior strategy. Experimental results show that the proposed method shows efficient results by significantly improving the visual effects of images in foggy weather. Also computational time of the existing techniques are much higher which has been overcame in this paper by using the proposed method. Qualitative assessment evaluation is performed on both benchmark and real time data sets for determining theefficacy of the technique used. Finally, the whole work is concluded with its relative advantages and shortcomings.


2021 ◽  
Vol 35 (2) ◽  
pp. 111
Author(s):  
Baowei Wang ◽  
Bin Niu ◽  
Peng Zhao ◽  
Neal N. Xiong
Keyword(s):  

Author(s):  
Yuanfang Zhang ◽  
Jiangbin Zheng ◽  
Xuejiao Kou ◽  
Yefan Xie
Keyword(s):  

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