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Reseach Article

An Assessment on Brightness Preservation Techniques over Digital Image Processing

by Vaishali Ahirwar, Himanshu Yadav, Anurag Jain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 68 - Number 12
Year of Publication: 2013
Authors: Vaishali Ahirwar, Himanshu Yadav, Anurag Jain
10.5120/11630-7102

Vaishali Ahirwar, Himanshu Yadav, Anurag Jain . An Assessment on Brightness Preservation Techniques over Digital Image Processing. International Journal of Computer Applications. 68, 12 ( April 2013), 12-17. DOI=10.5120/11630-7102

@article{ 10.5120/11630-7102,
author = { Vaishali Ahirwar, Himanshu Yadav, Anurag Jain },
title = { An Assessment on Brightness Preservation Techniques over Digital Image Processing },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 68 },
number = { 12 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume68/number12/11630-7102/ },
doi = { 10.5120/11630-7102 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:27:38.444400+05:30
%A Vaishali Ahirwar
%A Himanshu Yadav
%A Anurag Jain
%T An Assessment on Brightness Preservation Techniques over Digital Image Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 68
%N 12
%P 12-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Digital image processing forms core research area with in computer science disciplines. Rapid growth of image processing technologies has been used digital images more and more prominent in our daily life. Brightness preservation is a technique of improving the image brightness so that the limitations contained in these images is used for various applications in a better way. The paper presents a review on using hybrid transformation means used combination of two transformation techniques first, curvelet transformation is used to identify the bright regions of the original image and second, discrete wavelet transformation used for reduce Noise and compressed the image for improve the quality of images and then the histogram equalization method is used to enhance the image brightness. Histogram Equalization technique is one of the most popular methods for image enhancement due to its simplicity and efficiency. This is a review on these methodologies by which it is possible to preserve the brightness more efficiently.

References
  1. P. Rajavel, "Image Dependent Brightness Preserving Histogram Equalization", IEEE Transactions on Consumer Electronics, Vol. 56, No. 2, May 2010.
  2. Rafael C. Gonzalez, and Richard E. Woods, "Digital Image Processing", 2nd edition, Prentice Hall, 2002.
  3. N. Sengee and H. K. Choi "Brightness Preserving Weight Clustering Histogram Equalization", IEEE Transactions on Consumer Electronics, Vol. 54, No. 3, AUGUST 2008.
  4. Yeong-Taeg Kim, "Contrast enhancement using brightness preserving bi-histogram equalization," IEEE Transactions on Consumer Electronics, vol. 43, no. 1, pp. 1-8, Feb. 1997.
  5. Yu Wan, Qian Chen and Bao-Min Zhang, "Image enhancement based on equal area dualistic sub-image histogram equalization method," IEEE Transactions on Consumer Electronics, vol. 45, no. 1, pp. 68-75, Feb. 1999.
  6. Soong-Der Chen, and Abd. Rahman Ramli, "Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation," IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1301-1309, Nov. 2003.
  7. Mary Kim and Min Gyo Chung, "Recursively Separated and Weighted Histogram Equalization for Brightness Preservation and Contrast Enhancement", IEEE Transactions on Consumer Electronics, vol. 54, no. 3, pp. 1389-1397, August 2008.
  8. D. Menotti, L. Najman, J. Facon, and A. A. Araujo, "Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving," Recursively Separated and Weighted Histogram Equalization For Brightness Preservation and Contrast Enhancement, vol. 53, no. 3, pp. 1186- 1194, Aug 2007.
  9. H. Ibrahim and N. S. P. Kong, "Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement," Recursively Separated and Weighted Histogram Equalization For Brightness Preservation and Contrast Enhancement, vol. 53, no. 4, pp. 1752-1758, Nov. 2007.
  10. Chao Wang and Zhongfu Ye, "Brightness Preserving Histogram Equalization with Maximum Entropy: A Variational Perspective", IEEE Transactions on Consumer Electronics, vol. 51, no. 4, pp. 1326-1334, November 2005.
  11. Md. Foisal Hussein and Mohammad Reza Alsharif, "Minimum Mean Brightness Error Dynamic Histogram Equalization For Brightness Preserving Image Contrast Enhancement", International Journal of Innovative Computing, Information and Control, vol. 5, no. 10 (A), pp. 3249-3260, October 2009.
  12. Xia odong Xie, Zaifeng Shi, Wei Guo, Suying Yao, "An Adaptive Image Enhancement Technique Based on Image Characteristic", 2nd International Congress on Image and Signal Processing, CISP'09, pp. 1-5, Oct. 2009.
  13. Hasan Demirel, Cagri Ozcinar, and Gholamreza Anbarjafari,"Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition", IEEE Geosciences' and Remote Sensing Letters, vol. 7, no. 2, pp. 333-337, April 2010.
  14. Hasanul Kabir, Abdullah Al-Wadud, and Oksam Chae, "Brightness Preserving Image Contrast Enhancement Using Weighted Mixture of Global and Local Transformation Functions", The International Arab Journal of Information Technology, vol. 7, no. 4, October 2010.
  15. Debdoot Sheet, Hrushikesh Grad, Amit Suveer, Manjunatha Mahadevappa, and Jyotirmoy Chatterjee, "Brightness Preserving Dynamic Fuzzy Histogram Equalization", IEEE Transactions on Consumer Electronics, vol. 56, no. 4, pp. 2475-2480, November 2010.
Index Terms

Computer Science
Information Sciences

Keywords

image processing Brightness Preservation Hybrid transformation Histogram Equalization