Comparative Analysis of Various Image Enhancement Techniques

Image Enhancement is simple and most appealing area among all the digital image processing techniques. The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image. Histogram equalization is one of the well known image enhancement technique. HE becomes a popular technique for contrast enhancement because this method is simple and effective. This paper represents review of some techniques in the area of image enhancement for brightness preservation as brightness preservation is in great demand in the consumer electronics field, when the image is effectively enhanced. Comparisons with the best available results are given in order to illustrate the best possible technique that can be used as powerful image enhancement. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), Normalized absolute error (NAE), contrast, correlation and visual quality to make real-time image-processing applications more feasible and easier.

[1]  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 .

[2]  Abd. Rahman Ramli,et al.  Preserving brightness in histogram equalization based contrast enhancement techniques , 2004, Digit. Signal Process..

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

[4]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[5]  F. Cheevasuvit,et al.  Contrast enhancement using multipeak histogram equalization with brightness preserving , 1998, IEEE. APCCAS 1998. 1998 IEEE Asia-Pacific Conference on Circuits and Systems. Microelectronics and Integrating Systems. Proceedings (Cat. No.98EX242).

[6]  Haidi Ibrahim,et al.  Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement , 2007, IEEE Transactions on Consumer Electronics.

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

[8]  Yeong-Taeg Kim,et al.  Quantized bi-histogram equalization , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[10]  Wenjun Wang,et al.  Application of adaptive histogram equalization to x-ray chest images , 1994, Other Conferences.

[11]  S. Pizer,et al.  An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. , 1988, IEEE transactions on medical imaging.

[12]  S.S.Y. Lau.  Global image enhancement using local information , 1994 .

[13]  Chao Wang,et al.  Brightness preserving histogram equalization with maximum entropy: a variational perspective , 2005, IEEE Transactions on Consumer Electronics.

[14]  KimYeong-Taeg.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[15]  Yu-Jin Zhang.  Improving the accuracy of direct histogram specification , 1992 .