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Reseach Article

Measuring the Performance of Image Contrast Enhancement Technique

by Dominic Asamoah, Emmanuel Ofori Oppong, Stephen Opoku Oppong, Juliana Danso
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 22
Year of Publication: 2018
Authors: Dominic Asamoah, Emmanuel Ofori Oppong, Stephen Opoku Oppong, Juliana Danso
10.5120/ijca2018917899

Dominic Asamoah, Emmanuel Ofori Oppong, Stephen Opoku Oppong, Juliana Danso . Measuring the Performance of Image Contrast Enhancement Technique. International Journal of Computer Applications. 181, 22 ( Oct 2018), 6-13. DOI=10.5120/ijca2018917899

@article{ 10.5120/ijca2018917899,
author = { Dominic Asamoah, Emmanuel Ofori Oppong, Stephen Opoku Oppong, Juliana Danso },
title = { Measuring the Performance of Image Contrast Enhancement Technique },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2018 },
volume = { 181 },
number = { 22 },
month = { Oct },
year = { 2018 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number22/30015-2018917899/ },
doi = { 10.5120/ijca2018917899 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:07:24.291605+05:30
%A Dominic Asamoah
%A Emmanuel Ofori Oppong
%A Stephen Opoku Oppong
%A Juliana Danso
%T Measuring the Performance of Image Contrast Enhancement Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 22
%P 6-13
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image enhancement is one of the key techniques in processing quality of images in systems. The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image. This technique provides a multitude of choices for improving the visual quality of images. This is the main reason that image enhancement is used in a huge number of applications with important challenges such as noise reduction, degradations, blurring etc. This paper focuses on three contrast enhancement techniques for image enhancement which are: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) which are then compared with the help of the eight (8) quality image measurement metrics which are: i.e. the Mean squared error (MSE), Root Mean squared error (RMSE), Peak signal noise ratio (PSNR), Mean absolute error (MAE), Signal to noise ratio (SNR), Image Quality Index (IQI), Similarity Index (SI) and Pearson Correlation Coefficient (r). The paper concluded that Histogram Equalization (HE), is the one best contrast enhancement technique, as it recorded high percentage values for all the eight (8) quality image measurement metrics. Overall, it was therefore recommended histogram equalization technique should be embedded in any system that processes on images and output them to humans, for making life-changing decisions

References
  1. Oakley, J. P., and Satherley, B. L. 1998. Improving image quality in poor visibility conditions using a physical model for contrast degradation. IEEE Transactions on Image Processing 7 , 167–179
  2. Rahman, Z., Jobson, D. J., , and Woodell, G. A. 1996. Multi-scale retinex for color image enhancement. IEEE International Conference on Image Processing.
  3. Boccignone, G., and Picariello, A. 1997. Multiscale contrast enhancement of medical images. Proceedings of ICASSP.
  4. Kim Y. T. (1997), “Contrast Enhancement Using Histogram Equalization,” IEEE Transactions on Consumer Electronics,Vol 43,No.1, pp.1-8.
  5. Madhu C. D. (2014), “Preserving Contrast in Histogram Equalization Based on Adaptive Enhancement Techniques,” Computer Journal of Digital Signal Processing, Vol. 14, No. 5, pp. 413-428, 2004.
  6. Hojat Y. Ali Z., Amirhossein R., (2008) “A Novel Approach for Contrast Enhancement Based on Histogram Equalization”. International Conference on Computer and Communication Engineering, ICCCE, pp. 256 – 260.
  7. Wang-Zhiming, TAO-Jianhua (2007), “A Fast Implementation of Adaptive Histogram Equalization” 2006 8th International Conference on Signal Processing .
  8. Rajesh G., Bhawna M., Sheetal G. (2011) “Histogram Equalization Techniques for Image Enhancement,” IJECT Vol. 2, Issue 1.
  9. Confucius (2013), “Pictures, its definition and Importance in Image processing’,” World academy of Science, Engineering and Technology.
  10. Jeong C. B., Kim . G., Kim T. S., Kim S. K. (2011), “Comparison of image enhancement methods for the effective diagnosis in successive whole-body bone scans”, Vol.24, No.3, pp. 424-36.
  11. Cheng H. D. Shi X. J. (2004). “A simple and effective histogram equalization approach to image enhancement,” Digital Signal Processing Vol.14,pp. 158–170,.
  12. Saritha K R (2016). “A Study on Image Enhancement Techniques and Performance Measuring Metrics” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 4
  13. Abhishek T., Pramil S. (2004) “Analysis of Color Contrast Enhancement Techniques”, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 4
  14. Gayathri S., Mohanapriya N., and Kalaavathi B., (2003) “Survey on Contrast Enhancement Techniques”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 1.
  15. Nandhini V., Pratheepa R., N. Anjana and V. Elamaran, (2015) “A Novel Approach for Contrast Enhancement using Image Classification and Subdivision based Histogram Equalization”, Indian Journal of Science and Technology, Vol. 8, Issue 29
Index Terms

Computer Science
Information Sciences

Keywords

Image Enhancement Contrast Histogram equalization Measurement Metrics Noise Reduction