The week's pick
Reseach Article
Images Enhancement with Brightness Preserving using MRHRBFN
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 40 - Number 7 |
| Year of Publication: 2012 |
| Authors: Narendra Singh Bagri, Sanjeev Sharma, Santosh Sahu |
10.5120/4976-7231
|
Narendra Singh Bagri, Sanjeev Sharma, Santosh Sahu . Images Enhancement with Brightness Preserving using MRHRBFN. International Journal of Computer Applications. 40, 7 ( February 2012), 22-26. DOI=10.5120/4976-7231
Abstract
In the image processing, Images Enhancement with Brightness Preserving has many methods likes HE(Histogram Equalization), MHE(Multi-Histogram Equalization), IDBPHE(Image Dependent Brightness Preserving Histogram Equalization). We are proposed a novel methodology for the enhancement of images MRHRBFN (Multi-Resolution Histogram with Radial Bias Function Network). Enhancement process using pixel independent multi histogram method and Radial Bias Function Network. In process of our methodology image are decompose in terms of subbands. The sub band division perform by Curvelet transform. The Curvelet Transform divides two types of bands as higher band and lower band. The separation band of frequency generates a multiple matrix for input of radial bias Function Network. We have radial Bias function network work in low band data, because higher band data preserve brightness of image. The lower frequency matrix calculates bias and proceed weight factor when The lower value of frequency matrix regret reaches the mean value of given image. Finally we get better enhance image in comparison of multi histogram equalization.
References
- P.Rajavel “Image Dependent Brightness Preserving Histogram Equalization”, IEEE Transactions on Consumer Electronics, Vol. 56, No. 2, May 2010.
- Yu Wan, Qian Chen and Bao-Min Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method,” IEEE Trans. Consumer Electron., vol. 45, no. 1, pp. 68-75,Feb. 1999.
- Yeong-Taeg Kim, “Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization,” IEEE Trans Consumer Electronics, vol.43, no. 1, pp. 1-8, Feb. 1997.
- Soong-Der Chen and Abd. Rahman Ramli ,Member IEEE“Contrast enhancement using recursive mean-separate histogram equalization for scalable.
- Gonzalez C. and Woods E., Digital Image Processing, Addison-Wesley, 1992.
- A. Rafael C. Gonzalez, and Richard E. Woods, “Digital Image Processing,” 2nd edition, Prentice Hall, 2002.
- Wang Y., Chen Q., and Zhang B., “Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method,” Computer Journal of IEEE Transactions on Consumer Electronics, vol. 45, no. 1, pp. 68-75, 1999.
- Andersen, Bjorn and Tom Fagerhaug. Root Cause Analysis; Simplified Tools and Techniques. Milwaukee, WI; ASQ Quality Press, 2000.
- William J. Mitchell. The Recon_gured Eye: Visual Truth in the Post-Photographic Era. MIT Press 1992.
- Andersen, Bjorn and Tom Fagerhaug. Root Cause Analysis; Simplified Tools and Techniques. Milwaukee, WI; ASQ Quality Press, 2000.
- Okes, Duke and Russ Westcott, eds. The Certified Qualiy Manager Handbook, Second Edition. Milwaukee, WI; ASQ Quality Press, 2001. “Brightness Preserving Image Contrast Enhancement Using Weighted Mixture of Global and Local Transformation Functions.
- E. Hadjidemetriou, M. D. Grossberg, and S. K. Nayar Computer Science, Columbia University, New York, NY 10027,” Multiresolution Histograms and their Use for Texture Classification”
- Qing Liu Yidong Yuan Xuemin Lin,” Multi-resolution Algorithms for Building Spatial Histograms”
- Hasanul Kabir1, Abdullah Al-Wadud2, and Oksam Chae,”Brightness Preserving Image Contrast Enhancement Using Weighted Mixture of Globaland Local Transformation Function”,
- Adrian G.Bors,Department of computer Science university of York”Introduction of the Radial Basis Function (RBF) Networks”.
- Rajesh Garg, Bhawna Mittal, Sheetal Garg “Histogram Equalization Techniques For Image Enhancement, Vol. 2, Issue 1, March 2011
- Hojat Yeganeh, Ali Ziaei, Amirhossein Rezaie,” A Novel Approach for Contrast Enhancement Based on Histogram Equalization”, Proceedings of the International Conference on Computer and Communication Engineering 2008, May 13-15, 2008 Kuala Lumpur, Malaysia
- Didar S. Sohi, S. S. Devgan,”Application to Enhance the Teaching and Understanding of Basic Image Processing Techniques”.
- Wuni xu, Lanxiang Zhong, Dingyuan Wang“Image Processing Based on Seamless Integration Technology Between Lab VIEW and MATLAB. International Conference on Information, Networking and Automation (ICINA), 2010.
- Yafei TIAN, Qingtao WAN, Fengjun WU,” Local Histogram Equalization Based on the Minimum Brightness Error”, Fourth International Conference on Image and Graphics.
- H.D. Cheng ? and X.J. Shi ,A simple and effective histogram equalization approach to image enhancement. .
- Brassard, M. (1988). The Memory Jogger, A Pocket Guide of Tools for Continuous Improvement, pp. 36 - 43. Methuen, MA: GOAL/QPC.
- Department of the Navy (November 1992), Fundamentals of Total Quality Leadership (Instructor Guide), pp. 6-44 - 6-47. San Diego, CA: Navy Personnel Research and Development Center.
- Eric Wharton, Dr. Karen Panetta, Dr. Sos Agaian,” human visual system based multi-histogram equalization for nonuniform illumination and shadow correction”
- D. Menotti, L. Najman, J. Facon and A. A. Araujo, “Multi-histogram equalization methods for contrast enhancement and brightness.
- Soong-Der Chen, Abd. Rahman Ramli, Member, IEEE “Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement”, IEEE Transactions on Consumer Electronics, Vol. 49, No. 4, NOVEMBER 2003.
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