CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Comparison of Fuzzy Contrast Enhancement Techniques

by G. Sudhavani, M. Srilakshmi, P. Venkateswara Rao, K. Satya Prasad
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 22
Year of Publication: 2014
Authors: G. Sudhavani, M. Srilakshmi, P. Venkateswara Rao, K. Satya Prasad
10.5120/16728-6986

G. Sudhavani, M. Srilakshmi, P. Venkateswara Rao, K. Satya Prasad . Comparison of Fuzzy Contrast Enhancement Techniques. International Journal of Computer Applications. 95, 22 ( June 2014), 26-31. DOI=10.5120/16728-6986

@article{ 10.5120/16728-6986,
author = { G. Sudhavani, M. Srilakshmi, P. Venkateswara Rao, K. Satya Prasad },
title = { Comparison of Fuzzy Contrast Enhancement Techniques },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 22 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume95/number22/16728-6986/ },
doi = { 10.5120/16728-6986 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:20:36.442295+05:30
%A G. Sudhavani
%A M. Srilakshmi
%A P. Venkateswara Rao
%A K. Satya Prasad
%T Comparison of Fuzzy Contrast Enhancement Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 22
%P 26-31
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The aim of the image enhancement is to improve the interpretability or perception of the information in images for human viewers, or to provide 'better' input for other automated image processing techniques. It is an indispensable tool for researchers in wide verity of fields including art studies, medical imaging, forensics and atmospheric sciences. Most of images like satellite images, medical images and even real life photographs may suffer from poor contrast due to the inadequate or insufficient lighting during image acquiring. So it is necessary to enhance the contrast of an image. In this paper two enhancement techniques namely fuzzy rule based contrast enhancement, and contrast enhancement using intensification operator (INT) are presented for the low contrast grayscale images. In first technique fuzzy system response function is obtained by simple if-then rules, and in second technique the fuzzy contrast intensification operator is taken as a tool for the enhancement in the fuzzy property domain. Comparative analysis of these enhancement techniques is carried out by means of index of fuzziness (IOF) and processing time.

References
  1. G. Sudhavani, G. Madhuri, P. Venkateswara Rao & K. Satya Prasad "Removing of Gaussian Noise from Color Images by Varying Size of Fuzzy Filter", International Journal of Computer applications (09751 – 8887), vol. 72, No. 17, June 2013.
  2. G. Sudhavani, G. Madhuri, P. Venkateswara Tao & Dr. K. Satya Prasad "ADDITIVE NOISE REMOVAL FOR COLOR IMAGES USING FUZZY FILTERS", International Journal of Computer Science, Engineering Applications (IJCSEA) vol. 3, No. 3, June 2013.
  3. Etiene E. Kerre and Mike Nachtegael "Fuzzy Techniques in Image Processing" Studies in fuzziness and soft computing, ISSN 1434-9922, 2000.
  4. Byoung-Woo Yoon, Woo-Jin Song "Image contrast enhancement based on the generalized histogram" Journal of Electronic Imaging 16(3), 033005 (July – Sep 2007).
  5. Dr. H. Mamatha Devi, S. Somorjeet Singh, Th. Tangkeshwar Singh, O. Imocha Singh "A New Easy Method of Enhancement of Low Contrast Image using Spatial Domain" International Journal of Computer Applications (0975 – 8887) vol. 40, No. 1, February 2012.
  6. G. Sudhavani & Dr. K. Satya Prasad "Segmentation of Lip image by Modified Fuzzy C-means Clustering Algorithm" International Journal of Computer Science & Network Security, vol. 9, No. 4, April 2009.
  7. G. Sudhavani, S. Sravani, P. Venkateswara Rao & K. Satya Prasad "Fuzzy Edge Linking Process on Fuzzy Noise Filtered Image", International Journal of Computer Applications (0975-8887), vol. 93, No. 15, May 2014.
  8. Raman Maini and Himanshu Aggarwa "A Comprehensive Review of Image Enhancement Techniques" JOURNAL OF COMPUTING VOLUME 2, ISSIE 3, MARCH 2010, ISSN 2151 – 9167.
  9. Khairunnisa Hasikin & Nor Ashidi Mat Isa "Enhancement of the low contrast image using fuzzy set theory", 14th International Conference on Modeling and Simulation, IEEE, 2012.
  10. E. E. Kerre, Fuzzy sets and Approximate Reasoning, Xian, China: Xian Jiaotong Univ. Press, 1998.
  11. R. C. Gonzalez, R. E. Woods, Digital Image Processing",3rd Ed. , Prentice Hall,
  12. W. Pedrycz, F. Gomode "An Introduction to Fuzzy Sets: Analysis and Design", the MIT Press, 1998.
  13. ZADEH. L. A, "Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges", J. Cybern. , vol. 2, pp. 4-34, 1972.
  14. SANKAR K. PAL, ROBERT A. KING, "Image Enhancement using Smoothing with Fuzzy Sets", IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-11, No. 1, pp. 494-501, July 1981.
  15. SANKAR K. PAL, ROBERT A. KING, "On edge detection of X-ray images using fuzzy sets", IEEE Transactions on pattern Analysis and Machine Intelligence, vol. PAMI-5, No. 1, pp. 69-77, 1983.
  16. KAUFMANN, A. , "Introduction to the Theory of Fuzzy Sub-Sets-Fundamental Theoretical Elements", vol. 1, Academic Press, New York, 1975.
  17. SANKAR K. PAL, "Fuzziness, Image Information and Scene Analysis", in: Zadeh. L. A, Yager. R. R, "An Introduction to Fuzzy Logic Applications in Intelligent Systems", Kluwer Academic Publishers, USA, 1996.
  18. SANKAR K. PAL, KUNDU, M. K, "Automatic selection of object enhancement operator with quantitative justification based on fuzzy set theoretic measures, Pattern Recognition Letters", vol. 11, pp. 811-829, 1990.
Index Terms

Computer Science
Information Sciences

Keywords

Image enhancement contrast enhancement fuzzy logic membership function intensification operator fuzzy expected value fuzzifiers index of fuzziness processing time.

Powered by PhDFocusTM