Haze removal and fuzzy based enhancement of image

The quality of outdoor captured images by camera may be degraded due to the occurrence of haze in the atmosphere. The process of enhancing the image by removing the haze is call dehazing. In this paper, dark channel prior and fuzzy enhancement based method is applied to remove haze from a hazy image. The performance of the proposed method is evaluated by comparing average information content and natural image quality evaluator with two state-of-the-art algorithms. A comparative study and quantitative evaluation with existing algorithms demonstrate that similar even better quality results can be obtained by the proposed methods. The experimental result demonstrates that the proposed method removes the haze from a hazy image and also enhances it by maintaining the contrast.

[1]  Akihiro Higashi,et al.  Fast Single-image Defogging , 2014 .

[2]  Shree K. Nayar,et al.  Vision in bad weather , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[3]  M. S. Hitam,et al.  Mixture contrast limited adaptive histogram equalization for underwater image enhancement , 2013, 2013 International Conference on Computer Applications Technology (ICCAT).

[4]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[5]  Ke Lu,et al.  Single image dehazing with a physical model and dark channel prior , 2015, Neurocomputing.

[6]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[7]  V. Magudeeswaran,et al.  Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement , 2013 .

[8]  Raanan Fattal,et al.  Single image dehazing , 2008, ACM Trans. Graph..

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

[10]  Yoav Y. Schechner,et al.  Blind Haze Separation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Gaofeng Meng,et al.  Efficient Image Dehazing with Boundary Constraint and Contextual Regularization , 2013, 2013 IEEE International Conference on Computer Vision.

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

[13]  Kazuhiko Takahashi,et al.  Laser-based pedestrian tracking in outdoor environments by multiple mobile robots , 2011 .

[14]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.

[15]  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.

[16]  Shree K. Nayar,et al.  Chromatic framework for vision in bad weather , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[17]  Dapeng Li,et al.  Physics-based fast single image fog removal , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[18]  Davud Asemani,et al.  A robust adaptive algorithm of moving object detection for video surveillance , 2014, EURASIP J. Image Video Process..