Optimized contrast enhancement for real-time image and video dehazing

A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in general, we restore the hazy image by enhancing its contrast. However, the overcompensation of the degraded contrast may truncate pixel values and cause information loss. Therefore, we formulate a cost function that consists of the contrast term and the information loss term. By minimizing the cost function, the proposed algorithm enhances the contrast and preserves the information optimally. Moreover, we extend the static image dehazing algorithm to real-time video dehazing. We reduce flickering artifacts in a dehazed video sequence by making transmission values temporally coherent. Experimental results show that the proposed algorithm effectively removes haze and is sufficiently fast for real-time dehazing applications.

[1]  Cosmin Ancuti,et al.  A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image , 2010, ACCV.

[2]  Jizhou Sun,et al.  Video dehazing with spatial and temporal coherence , 2011, The Visual Computer.

[3]  장훈,et al.  [서평]「Computer Organization and Design, The Hardware/Software Interface」 , 1997 .

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

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

[6]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[7]  Barbara Chapman,et al.  Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation) , 2007 .

[8]  Soo-Chang Pei,et al.  Temporal Frequency of Flickering-Distortion Optimized Video Halftoning for Electronic Paper , 2011, IEEE Transactions on Image Processing.

[9]  John P. Oakley,et al.  Correction of Simple Contrast Loss in Color Images , 2007, IEEE Transactions on Image Processing.

[10]  Albert A. Michelson,et al.  Studies in Optics , 1995 .

[11]  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.

[12]  Chang-Su Kim,et al.  Single image dehazing based on contrast enhancement , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

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

[15]  David A. Patterson,et al.  Computer Organization and Design, Fourth Edition, Fourth Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design) , 2008 .

[16]  Jean-Philippe Tarel,et al.  Improved visibility of road scene images under heterogeneous fog , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[17]  Silong Peng,et al.  Single color image dehazing using sparse priors , 2010, 2010 IEEE International Conference on Image Processing.

[18]  Lee-Sup Kim,et al.  An advanced contrast enhancement using partially overlapped sub-block histogram equalization , 2001, IEEE Trans. Circuits Syst. Video Technol..

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

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

[21]  Qingmin Liao,et al.  Fast single image fog removal using edge-preserving smoothing , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[23]  Chang-Su Kim,et al.  Temporally x real-time video dehazing , 2012, 2012 19th IEEE International Conference on Image Processing.

[24]  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.

[25]  Sabine Süsstrunk,et al.  Color image dehazing using the near-infrared , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[26]  Boon-Lock Yeo,et al.  Rapid scene analysis on compressed video , 1995, IEEE Trans. Circuits Syst. Video Technol..

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

[28]  Dani Lischinski,et al.  Deep photo: model-based photograph enhancement and viewing , 2008, SIGGRAPH Asia '08.

[29]  Peter Carr,et al.  Improved Single Image Dehazing Using Geometry , 2009, 2009 Digital Image Computing: Techniques and Applications.

[30]  J. Rovamo,et al.  Flicker sensitivity as a function of target area with and without temporal noise , 2000, Vision Research.

[31]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[32]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[33]  E. Peli.  Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.