A Review on Methods of Image Dehazing

Literature survey is an important for understanding and gaining much more knowledge about the specific area of a subject. The outdoor images captured in inclement weather are degraded due to the presence of haze, fog, rain and so on. Images of scenes captured in bad weather have poor contrasts and colors. This may cause difficulty in detecting the objects in the captured hazy images. Due to haze there is a trouble to many computer vision applications as it diminishes the visibility of the scene. This paper presents a study about different image dehazing methods to remove the haze from the hazy images captured in real world weather conditions to recover a fast and improved quality of haze free images. There is a improvement in terms of contrast, visible range and color fidelity. All these techniques are widely used in many applications such as outdoor Surveillance, object detection, underwater images, etc. General Terms Bad weather conditions, Haze, Airlight, Direct attenuation, contrast, color fidelity, Haze model.

[1]  Shih-Chia Huang,et al.  Visibility Restoration of Single Hazy Images Captured in Real-World Weather Conditions , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Vinkey Sahu.  A Survey Paper On Single Image Dehazing , 2015 .

[3]  Bharat Bhushan,et al.  A Comparison of Various Defogging Techniques , 2014 .

[4]  Joonki Paik,et al.  Spatially adaptive image defogging using edge analysis and gradient-based tone mapping , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[5]  Shree K. Nayar,et al.  Instant dehazing of images using polarization , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Mohinder Malhotra.  Single Image Haze Removal Using Dark Channel Prior , 2016 .

[7]  Zheng Guo,et al.  Improved single image dehazing using guided filter , 2011 .

[8]  Xie Yaoqin,et al.  An Improved Single Image Haze Removal Algorithm Based on Dark Channel Prior and Histogram Specification , 2013, ICMT 2013.

[9]  Bo Wu,et al.  Improved single image dehazing using dark channel prior , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[10]  Yoav Y Schechner,et al.  Polarization-based vision through haze. , 2008, Applied optics.

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

[12]  D. Ji,et al.  SINGLE IMAGE DEHAZING FOR VISIBILITY IMPROVEMENT , 2015 .

[13]  Dilraj Kaur,et al.  A Critical Study and Comparative Analysis of Various Haze Removal Techniques , 2015 .

[14]  Shree K. Nayar,et al.  Vision and Rain , 2007, International Journal of Computer Vision.

[15]  Sudipta Mukhopadhyay,et al.  Single image fog removal using anisotropic diffusion , 2012 .

[16]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[17]  S. Mukhopadhyay,et al.  Single image fog removal using bilateral filter , 2012, 2012 IEEE International Conference on Signal Processing, Computing and Control.

[18]  P. C. Vashist,et al.  A Hybrid Defogging Technique based on Anisotropic Diffusion and IDCP using Guided Filter , 2015 .

[19]  Bingquan Huo,et al.  Image Dehazing with Dark Channel Prior and Novel Estimation Model , 2015, MUE 2015.

[20]  Hu Wei,et al.  Improved Single Image Dehazing Using Dark Channel Prior , 2010 .

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