Fusion Enhancement of Color Image Based on Global Histogram Equalization

A novel color image enhancement algorithm based on global histogram equalization (HE) was proposed, considering that global histogram equalization is easy to produce color distortion when color images are enhanced. Firstly each component of original RGB image is implemented global histogram equalization separately. Secondly original RGB image and equalized RGB image are all executed wavelet transform, and wavelet detail coefficients of the former are replaced by those of the latter, at that time wavelet approximate coefficients of original image are hold the line. Finally wavelet reconstructed image and original image are fused. The experiment results demonstrate that our algorithm not only inherits contrast improvement power of global histogram equalization, but also overcomes its defect being easy to bring color distortion. Furthermore, computation complexity is simple and any parameter needs not to be specified for the algorithm. So it is an efficient adaptive algorithm for color image enhancement.

[1]  John M. Tyler,et al.  Applying multiresolution methods to medical image enhancement , 2006, ACM-SE 44.

[2]  Jian Fan,et al.  Mammographic feature enhancement by multiscale analysis , 1994, IEEE Trans. Medical Imaging.

[3]  Guoping Qiu,et al.  Novel histogram processing for colour image enhancement , 2004, Third International Conference on Image and Graphics (ICIG'04).

[4]  Rangaraj M. Rangayyan,et al.  Histogram equalization of color images using the adaptive neighborhood approach , 1999, Electronic Imaging.

[5]  Guillermo Sapiro,et al.  Color histogram equalization through mesh deformation , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[6]  Karen O. Egiazarian,et al.  An Approach to Adaptive Enhancement of Diagnostic X-Ray Images , 2003, EURASIP J. Adv. Signal Process..

[7]  Dennis M. Healy,et al.  Contrast enhancement via multiscale gradient transformation , 1994, Proceedings of 1st International Conference on Image Processing.

[8]  Paul Scheunders,et al.  Wavelet-based enhancement and denoising using multiscale structure tensor , 2002, Proceedings. International Conference on Image Processing.

[9]  Dennis M. Healy,et al.  Contrast enhancement of medical images using multiscale edge representation , 1994, Defense, Security, and Sensing.

[10]  Yinpeng Jin,et al.  Regularization of PET reconstruction using multi-scale adaptive thresholding , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  G. Bellala.  Characterization of Signals from Multi scale Edges , 2009 .

[12]  J. Todd.  Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

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

[14]  Panos Trahanias,et al.  Color image enhancement through 3-D histogram equalization , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.