Fast and robust wavelet-based dynamic range compression and contrast enhancement model with color restoration

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a good candidate for real time video processing applications. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and high image quality.

[1]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

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

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

[4]  S. Pizer,et al.  Adaptive grey level assignment in CT scan display. , 1984, Journal of computer assisted tomography.

[5]  A Hurlbert,et al.  Formal connections between lightness algorithms. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[6]  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.

[7]  Vijayan K. Asari,et al.  An Adaptive and Non Linear Technique for Enhancement of Extremely High Contrast Images , 2006, 35th IEEE Applied Imagery and Pattern Recognition Workshop (AIPR'06).

[8]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[9]  William J. Dallas,et al.  Artifact Suppression in Digital Chest Radiographs Enhanced With Adaptive Histogram Equalization , 1989, Medical Imaging.

[10]  Re Gonzalez,et al.  R.C. Eddins, Digital image processing using MATLAB, vol. Gatesmark Publishing Knoxville , 2009 .

[11]  Zia-ur Rahman,et al.  Multi-scale retinex for color image enhancement , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[12]  Vijayan K. Asari,et al.  Image enhancement for improving face detection under non-uniform lighting conditions , 2008, 2008 15th IEEE International Conference on Image Processing.

[13]  Karin M. Hartzell,et al.  A Psychophysical Comparison of Two Methods for Adaptive Histogram Equalization , 1989, Medical Imaging.

[14]  Vijayan K. Asari,et al.  Fast and robust wavelet-based dynamic range compression with local contrast enhancement , 2008, SPIE Defense + Commercial Sensing.

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

[16]  Andrew F. Laine,et al.  Contrast enhancement by multiscale adaptive histogram equalization , 2001, SPIE Optics + Photonics.

[17]  Vijayan K. Asari,et al.  An illuminance-reflectance nonlinear video enhancement model for homeland security applications , 2005, 34th Applied Imagery and Pattern Recognition Workshop (AIPR'05).

[18]  Vijayan K. Asari,et al.  Nonlinear Enhancement of Extremely High Contrast Images for Visibility Improvement , 2006, ICVGIP.

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

[20]  H. Kolb.  How the Retina Works , 2003, American Scientist.

[21]  Vijayan K. Asari,et al.  Adaptive and integrated neighborhood-dependent approach for nonlinear enhancement of color images , 2005, J. Electronic Imaging.

[22]  Brian V. Funt,et al.  Analysis and Improvement of Multi-Scale Retinex , 1997, CIC.

[23]  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..

[24]  Vijayan K. Asari,et al.  Design of an efficient architecture for real-time image enhancement based on a luma-dependent nonlinear approach , 2004, International Conference on Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004..

[25]  Berthold K. P. Horn,et al.  Determining lightness from an image , 1974, Comput. Graph. Image Process..

[26]  Zia-ur Rahman,et al.  Statistics of visual representation , 2002, SPIE Defense + Commercial Sensing.

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