Fast centre- surround contrast modification

A new algorithm for fast contrast modification of standard dynamic range (SDR) images (8 bits/ channel) is presented. Its thrust is to enhance the contrast in the under-/over-exposed regions of SDR images, caused by the low dynamic range of the capturing device. It is motivated by the attributes of the shunting centre - surround cells of the human visual system. The main advantage of the proposed algorithm is its O(N ) complexity which results in very fast execution, even when executed on a conventional personal computer (0.2 s/frame for a 640 � 480 pixel resolution on a 3 GHz Pentium 4). Thus, it moderately increases the computational burden if it is used as a pre-processing stage for other image processing algorithms. The proposed method is compared with other established algorithms, which can enhance the contrast in the under-/over-exposed regions of SDR images: the multi-scale Retinex with colour rendition, the McCann Retinex (McCann99), the rational mapping function and the automatic colour equalisation. The results obtained by this comparison indicate that the proposed algorithm exhibits at least comparable results in contrast modification tasks to the other algorithms, in significantly reduced execution times.

[1]  Fionn Murtagh,et al.  Gray and color image contrast enhancement by the curvelet transform , 2003, IEEE Trans. Image Process..

[2]  Werner Blohm,et al.  Lightness determination at curved surfaces with applications to dynamic range compression and model-based coding of facial images , 1997, IEEE Trans. Image Process..

[3]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[4]  Hans-Peter Seidel,et al.  Perceptual evaluation of tone mapping operators with real-world scenes , 2005, IS&T/SPIE Electronic Imaging.

[5]  E H Land,et al.  An alternative technique for the computation of the designator in the retinex theory of color vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Guoping Qiu,et al.  Fast tone mapping for high dynamic range images , 2004, ICPR 2004.

[7]  Robert Sobol,et al.  Improving the Retinex algorithm for rendering wide dynamic range photographs , 2002, IS&T/SPIE Electronic Imaging.

[8]  Brian V. Funt,et al.  Luminance-Based Multi-Scale Retinex , 1997 .

[9]  Carlo Gatta,et al.  A new algorithm for unsupervised global and local color correction , 2003, Pattern Recognit. Lett..

[10]  Alessandro Rizzi,et al.  Mathematical definition and analysis of the retinex algorithm. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Erik Reinhard,et al.  Photographic tone reproduction for digital images , 2002, ACM Trans. Graph..

[12]  Karol Myszkowski,et al.  Design of a tone mapping operator for high-dynamic range images based upon psychophysical evaluation and preference mapping , 2003, IS&T/SPIE Electronic Imaging.

[13]  S. Grossberg,et al.  Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off-surround networks , 1975, Biological Cybernetics.

[14]  Alan Chalmers,et al.  Evaluation of tone mapping operators using a High Dynamic Range display , 2005, SIGGRAPH 2005.

[15]  Laurence Meylan.  Tone mapping for high dynamic range images , 2006 .

[16]  John J. McCann,et al.  Tuning Retinex parameters , 2002, IS&T/SPIE Electronic Imaging.

[17]  John J. McCann,et al.  Retinex in MATLABTM , 2004, J. Electronic Imaging.

[18]  Michael Elad,et al.  Reduced complexity Retinex algorithm via the variational approach , 2003, J. Vis. Commun. Image Represent..

[19]  J. Alex Stark,et al.  Adaptive image contrast enhancement using generalizations of histogram equalization , 2000, IEEE Trans. Image Process..

[20]  Christine D. Piatko,et al.  A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes , 1997, IEEE Trans. Vis. Comput. Graph..

[21]  Alessandro Rizzi,et al.  Tuning Retinex for HDR Images Visualization , 2004, CGIV.

[22]  Laurence Meylan,et al.  High dynamic range image rendering with a retinex-based adaptive filter , 2006, IEEE Transactions on Image Processing.

[23]  Michael Wimmer,et al.  Image Attributes and Quality for Evaluation of Tone Mapping Operators , 2006 .

[24]  Brian V. Funt,et al.  Investigations into Multi-Scale Retinex , 1998 .

[25]  RushmeierHolly,et al.  A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes , 1997 .

[26]  Christophe Schlick,et al.  Quantization Techniques for Visualization of High Dynamic Range Pictures , 1995 .

[27]  Laurence Meylan,et al.  Bio-inspired color image enhancement , 2004, IS&T/SPIE Electronic Imaging.

[28]  Alessandro Rizzi,et al.  Random Spray Retinex: A New Retinex Implementation to Investigate the Local Properties of the Model , 2007, IEEE Transactions on Image Processing.

[29]  Michael Elad,et al.  Variational famework for Retinex , 2002, IS&T/SPIE Electronic Imaging.

[30]  Stephen Grossberg,et al.  A neural network for enhancing boundaries and surfaces in synthetic aperture radar images , 1999, Neural Networks.

[31]  Alessandro Rizzi,et al.  Unsupervised corrections of unknown chromatic dominants using a Brownian-path-based Retinex algorithm , 2003, J. Electronic Imaging.

[32]  Laurence Meylan,et al.  HDR CFA image rendering , 2006, 2006 14th European Signal Processing Conference.

[33]  Michael Elad,et al.  A Variational Framework for Retinex , 2002, IS&T/SPIE Electronic Imaging.

[34]  C. Gatta,et al.  Local linear LUT method for spatial colour-correction algorithm speed-up , 2006 .

[35]  Brian V. Funt,et al.  Tuning Retinex parameters , 2004, J. Electronic Imaging.

[36]  Toyokazu Mizoguchi.  Evaluation of Image Sensors , 2005 .

[37]  Hiroshi Yamaguchi,et al.  Testing HDR Image Rendering Algorithms , 2004, CIC.

[38]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[39]  Sebastiano Battiato,et al.  High dynamic range imaging for digital still camera: an overview , 2003, J. Electronic Imaging.

[40]  John P. Oakley,et al.  Advantages of multiscale product filters for dynamic range compression in images , 2006 .

[41]  John J. McCann,et al.  Lessons Learned from Mondrians Applied to Real Images and Color Gamuts , 1999, CIC.

[42]  Zia-ur Rahman,et al.  Retinex processing for automatic image enhancement , 2004, J. Electronic Imaging.

[43]  Carlo Gatta,et al.  From Retinex to Automatic Color Equalization: issues in developing a new algorithm for unsupervised color equalization , 2004, J. Electronic Imaging.