Single-image haze removal using the mean vector L2-norm of RGB image sample window

Abstract Poor visibility in bad weather is a major problem for many applications of computer vision. This paper presents an efficient single RGB image haze removal algorithm to improve image visibility. The proposed algorithm has three parts. Firstly, we estimate the initial transmission map, which is key to single-image haze removal. Different form dark channel-based method, the proposed method estimates this map by computing the mean vector L2-norm of the sample window. Then, the algorithm refines the estimated transmission map using a guidance filter. Secondly, we estimate the atmospheric light using the RGB value of the pixel corresponding to the maximum value of the L2-norm of the sample windows. Thirdly, the proposed algorithm improves the recovery approach for clear image restoration. This means that it can directly recover sufficiently bright images. Experimental results show that four parameters containing the radius of the sample window for the transmission map estimate, the guidance filter radius for refining the transmission, and the threshold and coefficient used to restore the clear image have important effects on the defogging results. Compared with the dark channel-based method, the proposed method results in brighter and more natural defogged images.

[1]  Raanan Fattal.  Single image dehazing , 2008, SIGGRAPH 2008.

[2]  Truong Q. Nguyen,et al.  Hazy image modeling using color ellipsoids , 2011, 2011 18th IEEE International Conference on Image Processing.

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

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

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

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

[7]  Fabio Gagliardi Cozman,et al.  Depth from scattering , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[9]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Chaomin Shen,et al.  Single image dehazing and denoising with variational method , 2010, 2010 International Conference on Image Analysis and Signal Processing.

[11]  Ding Meng,et al.  Efficient dark channel based image dehazing using quadtrees , 2013 .

[12]  Gudrun Klinker,et al.  A physical approach to color image understanding , 1989, International Journal of Computer Vision.

[13]  M. Werman,et al.  Color lines: image specific color representation , 2004, CVPR 2004.

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

[15]  Truong Q. Nguyen,et al.  An Investigation of Dehazing Effects on Image and Video Coding , 2012, IEEE Transactions on Image Processing.

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

[17]  Hanseok Ko,et al.  Fast Single Image De-Hazing Using Characteristics of RGB Channel of Foggy Image , 2013, IEICE Trans. Inf. Syst..

[18]  Truong Q. Nguyen,et al.  An analysis of single image defogging methods using a color ellipsoid framework , 2013, EURASIP J. Image Video Process..

[19]  Monson H. Hayes,et al.  Adaptive defogging with color correction in the HSV color space for consumer surveillance system , 2012, IEEE Transactions on Consumer Electronics.

[20]  Dani Lischinski,et al.  Deep photo: model-based photograph enhancement and viewing , 2008, SIGGRAPH 2008.

[21]  Adil Masood Siddiqui,et al.  Contrast Enhancement in Poor Visibility Conditions Using Guided Filtering , 2013 .