Instant dehazing of images using polarization

We present an approach to easily remove the effects of haze from images. It is based on the fact that usually airlight scattered by atmospheric particles is partially polarized. Polarization filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, works under a wide range of atmospheric and viewing conditions. We analyze the image formation process, taking into account polarization effects of atmospheric scattering. We then invert the process to enable the removal of haze from images. The method can be used with as few as two images taken through a polarizer at different orientations. This method works instantly, without relying on changes of weather conditions. We present experimental results of complete dehazing in far from ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color. As a by product, the method also yields a range (depth) map of the scene, and information about properties of the atmospheric particles.

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

[2]  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).

[3]  KokKeong Tan,et al.  Enhancement of color images in poor visibility conditions , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

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

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

[6]  Andrew S. Glassner,et al.  Principles of Digital Image Synthesis , 1995 .

[7]  D. Stork,et al.  Polarized light in nature , 1985 .

[8]  Lawrence B. Wolff,et al.  Polarization vision: a new sensory approach to image understanding , 1997, Image Vis. Comput..

[9]  Paul S. Pencikowski Low-cost vehicle-mounted enhanced vision system comprised of a laser illuminator and range-gated camera , 1996, Defense, Security, and Sensing.

[10]  Katsushi Ikeuchi,et al.  Measurement of surface orientations of transparent objects using polarization in highlight , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[11]  Edward H. Adelson,et al.  Separating reflections and lighting using independent components analysis , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[12]  J Shamir,et al.  Polarization and statistical analysis of scenes containing a semireflector. , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  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.

[14]  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.

[15]  Barbara T. Sweet,et al.  Image processing and fusion for landing guidance , 1996, Defense, Security, and Sensing.

[16]  Norman S. Kopeika,et al.  A System Engineering Approach to Imaging , 1998 .

[17]  G. Rybicki Radiative transfer , 2019, Climate Change and Terrestrial Ecosystem Modeling.

[18]  C F Bohren Maximum degree of polarization of the resultant of two partially polarized incoherent beams. , 1987, Applied optics.

[19]  J E Solomon,et al.  Polarization imaging. , 1981, Applied optics.

[20]  Richard R. Brooks,et al.  Atmospheric attenuation reduction through multisensor fusion , 1998, Defense, Security, and Sensing.

[21]  Shree K. Nayar,et al.  Removing weather effects from monochrome images , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[23]  Yoav Y. Schechner,et al.  Polarization-based decorrelation of transparent layers: The inclination angle of an invisible surface , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.