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Reseach Article

A Comparison of Contrast Enhancement Techniques in Poor Illuminated Gray Level and Color Images

by Narasimhan K, Sudarshan C R, Nagarajan Raju
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 25 - Number 2
Year of Publication: 2011
Authors: Narasimhan K, Sudarshan C R, Nagarajan Raju
10.5120/3004-4045

Narasimhan K, Sudarshan C R, Nagarajan Raju . A Comparison of Contrast Enhancement Techniques in Poor Illuminated Gray Level and Color Images. International Journal of Computer Applications. 25, 2 ( July 2011), 17-25. DOI=10.5120/3004-4045

@article{ 10.5120/3004-4045,
author = { Narasimhan K, Sudarshan C R, Nagarajan Raju },
title = { A Comparison of Contrast Enhancement Techniques in Poor Illuminated Gray Level and Color Images },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 25 },
number = { 2 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 17-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume25/number2/3004-4045/ },
doi = { 10.5120/3004-4045 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:43.590725+05:30
%A Narasimhan K
%A Sudarshan C R
%A Nagarajan Raju
%T A Comparison of Contrast Enhancement Techniques in Poor Illuminated Gray Level and Color Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 25
%N 2
%P 17-25
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Morphological transformations (Opening by reconstruction, Erosion-Dilation method) and Block Analysis is used to detect the background of gray level and color images. These techniques are first implemented in gray scale and are then extended to color images by individually enhancing the color components. For aiding better results, the compressed domain (DCT) technique is used exclusively for color image enhancement. The major advantage of the DCT method is that it can be used for any type of illumination. In all the above methods, the enhancement of the background detected image is done using Weber’s law (modified Weber’s law for compressed domain). In this paper, a critical analysis of the various advantages and drawbacks in each method are performed and ways for overcoming the drawbacks are also suggested. Here, the results of each technique are illustrated for various backgrounds, majority of them in poor lighting condition. The tool used in this study is MATLAB. Finally the performance metrics like Entropy, Color Enhancement Factor (CEF) , JPEG Quality Metric (JPQM) , Wang Bovik Quality metric (WBQM) and Structural Similarity Index (SSIM) are calculated and compared for the results of each technique.

References
  1. Angélica R. Jiménez-Sánchez, Jorge D. Mendiola-Santibañez, Iván R. Terol-Villalobos, Gilberto Herrera-Ruíz, Damián Vargas-Vázquez, Juan J. García-Escalante, and Alberto Lara-Guevara “Morphological Background Detection and Enhancement of Images With Poor Lighting” , vol. 18, no. 3, March 2009.
  2. S. Mukhopadhyay and B. Chanda, “A multiscale morphological approach to local contrast enhancement,” Signal Processing, vol. 80, no. 4, pp. 685–696, 2000.
  3. C. R. González and E.Woods, Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall, 1992.
  4. S. Mukhopadhyay and B. Chanda, “A multiscale morphological approach to local contrast enhancement,” Signal Process, vol. 80, no. 4, pp. 685–696, 2000.
  5. J. D. Mendiola-Santibañez and I. R. Terol-Villalobos, “Morphological contrast mappingsj based on the flat zone notion,” Computación y Sistemas, vol. 6, pp. 25–37, 2002.
  6. A.Saradha Devi, S. Suja Priyadharsini, S. Athinarayanan, “A block based scheme for enhancing low luminated images”, The International journal of Multimedia & Its Applications (IJMA) Vol.2, No.3, August 2010
  7. Zhou Wang, Student Member, IEEE, and Alan C. Bovik, Fellow, IEEE “A Universal Quality Index”, IEEE signal processing lectures, Vol XX, No. Y, March 2002
  8. J. Kasperek, “Real time morphological image contrast enhancement in virtex FPGA,” in Lecture Notes in Computer Science. New York: Springer, 2004.
  9. S. Lee, “An efficient content-based image enhancement in the compressed domain using retinex theory,” IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 2, pp. 199–213, Feb. 2007
  10. J. Jiang and G. Feng, “The spatial relationships of DCT coefficients between a block and its subblocks,” IEEE Trans. Signal Process, vol. 50, no. 5, pp. 1160–1169, May 2002
  11. A. Majumder and S. Irani, “Perception-based contrast enhancement of images,” ACM Trans. Appl. Percpt, vol. 4, no. 3, 2007, Article 17.
  12. Jayanta Mukherjee, Senior Member, IEEE, and Sanjit K. Mitra, Life Fellow, IEEE “Enhancement of Color Images by Scaling the DCT Coefficients”, IEEE transactions on image processing, vol. 17, no. 10, October 2008
  13. J. Mukherjee and S. K. Mitra, “Arbitrary resizing of images in the DCT space,” IEEE Proc. Vis., Image, Signal Process., vol. 152, no. 2, pp. 155–164, 2005.
  14. J. Tang, E. Peli, and S. Acton, “Image enhancement using a contrast measure in the compressed domain,” IEEE Signal Process. Lett., vol. 10, no. 10, pp. 289–292, Oct. 2003.
  15. Z. Wang, H. R. Sheikh, and A. C. Bovik, “No-reference perceptual quality assessment of JPEG compressed images,” in Proc. Int. Conf. Image Processing, Rochester, NY, Sep. 2002, vol. I, pp. 477–480
Index Terms

Computer Science
Information Sciences

Keywords

Image acquisition Optimization problem DCT Weber’s law