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

Adaptive Sigmoid Function to Enhance Low Contrast Images

by Saruchi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 4
Year of Publication: 2012
Authors: Saruchi
10.5120/8747-2634

Saruchi . Adaptive Sigmoid Function to Enhance Low Contrast Images. International Journal of Computer Applications. 55, 4 ( October 2012), 45-49. DOI=10.5120/8747-2634

@article{ 10.5120/8747-2634,
author = { Saruchi },
title = { Adaptive Sigmoid Function to Enhance Low Contrast Images },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 4 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 45-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number4/8747-2634/ },
doi = { 10.5120/8747-2634 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:56:26.798313+05:30
%A Saruchi
%T Adaptive Sigmoid Function to Enhance Low Contrast Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 4
%P 45-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image enhancement is one of the most important issues in low-level image processing. Mainly, enhancement methods can be classified into two classes: global and local methods. Various enhancement schemes are used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization (HE). Histogram Equalization (HE) has proved to be a simple and effective image contrast enhancement technique. In this paper, the global histogram equalization is improved by using sigmoid function combined with local enhancement statistics. Experimental results demonstrate that the proposed method can enhance the images effectively. The performances of the existing techniques and the proposed method are evaluated in terms of SNR, PSNR, CoC.

References
  1. Hui Zhu, Francis H. Y. Chan, and F. K. Lam," Image Contrast Enhancement by Constrained Local Histogram Equalization", Computer Vision and Image Understanding ,Vol. 73, No. 2, February, pp. 281–290, 1999.
  2. R. C. Gonzalez and R. E. Woods," Digital Image Processing", 3rd edition, Prentice Hall, 2009.
  3. Naglaa Yehya Hassan and Norio Aakamatsu ,"Contrast Enhancement Technique of Dark Blurred Image", IJCSNS International Journal of Computer Science and Network Security, VOL. 6 No. 2A, February,pp. 223-226,2006.
  4. E. H. Hall, Almost uniform distribution for computer image enhancement, IEEE Trans. Comput. 23(2), 1974, 207–208.
  5. S. Annadurai and R. Shanmugalakshmi, "Fundamentals of Digital Image Processing",Pearson,2007.
  6. William K. Pratt(2007), Digital Image Processing , Los Altos, California.
  7. Bhabatosh Chanda and Dwijest Dutta Majumder, 2002, Digital Image Processing and Analysis.
  8. D. J. Ketchum, Real-time image enhancement techniques, Proc. SPIE/OSA, 1976, 120–125.
  9. R. A. Hummel, Image enhancement by histogram transformation, Computer Graphics Image Process. 6, 1977, 184–195.
  10. S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. H. Romeny, J. B. Zimmerman, and K. Zuiderveld, Adaptive histogram equalization and its variations, Comput. Vision Graphics Image Process. 39, 1987, 355–368.
  11. J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney, B. Brenton, "Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement," IEEE Tr. on Medical Imaging, pp. 304-312, Dec. 1988.
  12. S. Lau, "Global image enhancement using local information," Electronics Letters, vol. 30, pp. 122–123, Jan. 1994.
  13. Sunita Dhariwal "Comparative Analysis of Various Image Enhancement Techniques", IJECT Vol. 2, Issue 3, Sept. 2011.
  14. J. Hertz, R. Plamer,. Introduction to the neural computation, Addison Wesley, California, 1991.
  15. Naglaa Hassan,Norio Akamatsu,"A new approach for contrast enhancement using sigmoid function",The International Arab Journal of Information Technology,pp. 221-225,Vol. 1,No. 2,July2004.
  16. Dileep MD and A. Sreenivasa Murthy,"A Comparison between different Colour Image Contrast Enhancement Algorithms",IEEE, PROCEEDINGS OF ICETECT 2011,pp 708-712.
  17. Sonia Goyal,Seema," Region Based Contrast Limited Adaptive HE with Additive Gradient for Contrast Enhancement of Medical Images (MRI)", International Journal of Soft Computing and Engineering (IJSCE), Volume-1, Issue-4, pp. 154-157,September 2011.
  18. Jayaraman, S. Esakkirajan and T. Veerakumar," Digital Image Processing",Tata Mc-Graw Hill Education,2009.
Index Terms

Computer Science
Information Sciences

Keywords

contrast enhancement sigmoid function Histogram Equalization image processing

Powered by PhDFocusTM