Contrast enhancement by automatic and parameter-free piecewise linear transformation for color images

Conventional contrast enhancement methods have four shortcomings. First, most of them need transformation functions and parameters which are specified manually. Second, most of them are application-oriented methods. Third, most of them are performed on gray level images. Fourth, the histogram equalization (HE) based enhancement methods use non-linear transform function. Thus, this paper proposes an automatic and parameter-free contrast enhancement algorithm for color images. This method includes following steps: First, RGB color space is transformed to HSV color space. Second, image content analysis is used to analyze the image illumination distribution. Third, the original image is enhanced by piecewise linear based enhancement method. Finally, the enhancement image is transformed back to RGB color space. This novel enhancement is automatic and parameter-free. Our experiments included various color images with low and high contrast. Experiment results show that the performance of the proposed method is better than histogram equalization (HE) and its six variations in non-over enhancement and natural clearly revealed. Moreover, the proposed algorithm can be run on an embedded environment (such as mobile device, digital camera, or other consumer products) and processed in real-time system due to its simplicity and efficiently.

[1]  Soo-Chang Pei,et al.  Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis , 2004, IEEE Transactions on Image Processing.

[2]  B. N. Chatterji,et al.  Adaptive contrast enhancement for color images , 1997, Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat..

[3]  Hsi-Jian Lee,et al.  Binarization of color document images via luminance and saturation color features , 2002, IEEE Trans. Image Process..

[4]  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.

[5]  Abd. Rahman Ramli,et al.  Minimum mean brightness error bi-histogram equalization in contrast enhancement , 2003, IEEE Trans. Consumer Electron..

[6]  Abd. Rahman Ramli,et al.  Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation , 2003, IEEE Trans. Consumer Electron..

[7]  V. Leitáo,et al.  Computer Graphics: Principles and Practice , 1995 .

[8]  M. Abdullah-Al-Wadud,et al.  A Dynamic Histogram Equalization for Image Contrast Enhancement , 2007, 2007 Digest of Technical Papers International Conference on Consumer Electronics.

[9]  Shanq-Jang Ruan,et al.  Dynamic contrast enhancement based on histogram specification , 2005, IEEE Transactions on Consumer Electronics.

[10]  C. Munteanu,et al.  Color image enhancement using evolutionary principles and the Retinex theory of color constancy , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

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

[12]  Laurence Meylan,et al.  Color Image Enhancement Using A Retinex-Based Adaptive Filter , 2004, CGIV.

[13]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[14]  M. Carter Computer graphics: Principles and practice , 1997 .

[15]  David Menotti,et al.  Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving , 2007, IEEE Transactions on Consumer Electronics.

[16]  김정연,et al.  서브블록 히스토그램 등화기법을 이용한 개선된 콘트래스트 강화 알고리즘 ( An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization ) , 1999 .

[17]  William K. Pratt,et al.  Digital image processing, 2nd Edition , 1991, A Wiley-Interscience publication.

[18]  Chao Wang,et al.  Brightness preserving histogram equalization with maximum entropy: a variational perspective , 2005, IEEE Trans. Consumer Electron..

[19]  Haim Levkowitz,et al.  GLHS: A Generalized Lightness, Hue, and Saturation Color Model , 1993, CVGIP Graph. Model. Image Process..

[20]  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,.

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

[22]  Andrea Sanna,et al.  CMBFHE: a novel contrast enhancement technique based on cascaded multistep binomial filtering histogram equalization , 2006, IEEE Transactions on Consumer Electronics.

[23]  Qian Chen,et al.  Image enhancement based on equal area dualistic sub-image histogram equalization method , 1999, IEEE Trans. Consumer Electron..

[24]  I. M. Bockstein Color equalization method and its application to color image processing , 1986 .

[25]  Rae-Hong Park,et al.  High dynamic range for contrast enhancement , 2006, IEEE Transactions on Consumer Electronics.

[26]  Yeong-Taeg Kim,et al.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[27]  Arthur R. Weeks,et al.  Histogram equalization of 24-bit color images in the color difference (C-Y) color space , 1995, J. Electronic Imaging.

[28]  Rabab Kreidieh Ward,et al.  Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization , 2007, IEEE Transactions on Consumer Electronics.

[29]  Christopher C. Yang,et al.  Efficient gamut clipping for color image processing using LHS and YIQ , 2003 .