Fog removal algorithms: Survey and perceptual evaluation

In this paper, we evaluate three state-of-the-art fog removal algorithms. The goal of the evaluation is to compare which algorithm performs better with regards to human perception; this has not been done in previous works. Two approaches are used to evaluate these algorithms: one is computing the ratio between the gradient of the visible edges in the images before and after fog removal; another one is using a psychophysical method with human observers and a rank order protocol. Using both computing based and psychophysical based methods allows us to investigate whether they lead to comparable results. The experimental results presented in the paper gives a clear comparison outcome between the three tested algorithms.

[1]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[2]  Robby T. Tan,et al.  Visibility in bad weather from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Chengwu Cui Comparison of Two Psychophysical Methods for Image Color Quality Measurement: Paired Comparison and Rank Order , 2000, Color Imaging Conference.

[4]  Carlo Gatta,et al.  A new algorithm for unsupervised global and local color correction , 2003, Pattern Recognit. Lett..

[5]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, CVPR.

[6]  Jean-Philippe Tarel,et al.  Automatic fog detection and estimation of visibility distance through use of an onboard camera , 2006, Machine Vision and Applications.

[7]  Jean-Philippe Tarel,et al.  Fast visibility restoration from a single color or gray level image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Alessandro Rizzi,et al.  Spatio-Temporal Retinex-Inspired Envelope with Stochastic Sampling: A Framework for Spatial Color Algorithms , 2011 .

[9]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  The Best of IET and IBC , 2012 .

[11]  Liangpei Zhang,et al.  Single image haze removal considering sensor blur and noise , 2013, EURASIP J. Adv. Signal Process..

[12]  Majid Mirmehdi,et al.  Proceedings of the 11th European Conference on Computer Vision (ECCV2010) , 2010 .

[13]  Yong-Qin Zhang Visibility enhancement using an image filtering approach , 2012, EURASIP J. Adv. Signal Process..

[14]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[15]  Peter G. Engeldrum,et al.  Psychometric Scaling: A Toolkit for Imaging Systems Development , 2000 .

[16]  Andrew Zisserman,et al.  Progressive search space reduction for human pose estimation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Shree K. Nayar,et al.  Contrast Restoration of Weather Degraded Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Jean-Philippe Tarel,et al.  BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES , 2011 .