Non-sky polarization-based dehazing algorithm for non-specular objects using polarization difference and global scene feature.

Photographic images taken in foggy or hazy weather (hazy images) exhibit poor visibility and detail because of scattering and attenuation of light caused by suspended particles, and therefore, image dehazing has attracted considerable research attention. The current polarization-based dehazing algorithms strongly rely on the presence of a "sky area", and thus, the selection of model parameters is susceptible to external interference of high-brightness objects and strong light sources. In addition, the noise of the restored image is large. In order to solve these problems, we propose a polarization-based dehazing algorithm that does not rely on the sky area ("non-sky"). First, a linear polarizer is used to collect three polarized images. The maximum- and minimum-intensity images are then obtained by calculation, assuming the polarization of light emanating from objects is negligible in most scenarios involving non-specular objects. Subsequently, the polarization difference of the two images is used to determine a sky area and calculate the infinite atmospheric light value. Next, using the global features of the image, and based on the assumption that the airlight and object radiance are irrelevant, the degree of polarization of the airlight (DPA) is calculated by solving for the optimal solution of the correlation coefficient equation between airlight and object radiance; the optimal solution is obtained by setting the right-hand side of the equation to zero. Then, the hazy image is subjected to dehazing. Subsequently, a filtering denoising algorithm, which combines the polarization difference information and block-matching and 3D (BM3D) filtering, is designed to filter the image smoothly. Our experimental results show that the proposed polarization-based dehazing algorithm does not depend on whether the image includes a sky area and does not require complex models. Moreover, the dehazing image except specular object scenarios is superior to those obtained by Tarel, Fattal, Ren, and Berman based on the criteria of no-reference quality assessment (NRQA), blind/referenceless image spatial quality evaluator (BRISQUE), blind anistropic quality index (AQI), and e.

[1]  Nikolay N. Ponomarenko,et al.  Color image database TID2013: Peculiarities and preliminary results , 2013, European Workshop on Visual Information Processing (EUVIP).

[2]  J. Tyo.  Design of optimal polarimeters: maximization of signal-to-noise ratio and minimization of systematic error. , 2002, Applied optics.

[3]  Moshe Ben-Ezra.  Segmentation with invisible keying signal , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[4]  J. S. Tyo,et al.  Optimum linear combination strategy for an N-channel polarization-sensitive imaging or vision system , 1998 .

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

[6]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[7]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[8]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[9]  Edward N Pugh,et al.  Separation and contrast enhancement of overlapping cast shadow components using polarization. , 2006, Optics express.

[10]  Alan C. Bovik,et al.  A Two-Step Framework for Constructing Blind Image Quality Indices , 2010, IEEE Signal Processing Letters.

[11]  Nader Engheta,et al.  Display of polarization information by coherently moving dots. , 2003, Optics express.

[12]  Lawrence B. Wolff,et al.  Polarization phase-based method for material classification and object recognition in computer vision , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  John P. Oakley,et al.  Improving image quality in poor visibility conditions using a physical model for contrast degradation , 1998, IEEE Trans. Image Process..

[14]  J S Tyo,et al.  Target detection in optically scattering media by polarization-difference imaging. , 1996, Applied optics.

[15]  Jian Liang,et al.  Polarimetric dehazing method for dense haze removal based on distribution analysis of angle of polarization. , 2015, Optics express.

[16]  K. Ikeuchi,et al.  Measurement of surface orientations of transparent objects by use of polarization in highlight , 1999 .

[17]  Yoav Y Schechner,et al.  Skyless polarimetric calibration and visibility enhancement. , 2009, Optics express.

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

[19]  Ling Shao,et al.  A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior , 2015, IEEE Transactions on Image Processing.

[20]  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.

[21]  Jean-Philippe Tarel,et al.  Towards Fog-Free In-Vehicle Vision Systems through Contrast Restoration , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

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

[24]  K K Tan,et al.  Physics-based approach to color image enhancement in poor visibility conditions. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[25]  Ketan Tang,et al.  Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Alan C. Bovik,et al.  No-reference quality assessment using natural scene statistics: JPEG2000 , 2005, IEEE Transactions on Image Processing.

[27]  Lawrence B. Wolff,et al.  Using polarization to separate reflection components , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  Shai Avidan,et al.  Non-local Image Dehazing , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[30]  Codruta O. Ancuti,et al.  Single Image Dehazing by Multi-Scale Fusion , 2013, IEEE Transactions on Image Processing.

[31]  Xiaochun Cao,et al.  Single Image Dehazing via Multi-scale Convolutional Neural Networks , 2016, ECCV.

[32]  Ko Nishino,et al.  Factorizing Scene Albedo and Depth from a Single Foggy Image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  Shree K. Nayar,et al.  Polarization-based vision through haze , 2003 .

[34]  Hao Wang,et al.  Fast single image haze removal via local atmospheric light veil estimation , 2015, Comput. Electr. Eng..

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