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Enhancing Quality of Images using Fuzzy Logic and Singleton Parameters

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
J. B. Hayfron- Acquah, J. K. Panford, Y. Poakwah Gyimah

Hayfron- J B Acquah, J K Panford and Poakwah Y Gyimah. Enhancing Quality of Images using Fuzzy Logic and Singleton Parameters. International Journal of Computer Applications 165(13):7-13, May 2017. BibTeX

	author = {J. B. Hayfron- Acquah and J. K. Panford and Y. Poakwah Gyimah},
	title = {Enhancing Quality of Images using Fuzzy Logic and Singleton Parameters},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {165},
	number = {13},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {7-13},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017913991},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


There are a lot of images around carrying valuable information yet because of its poor quality, necessary information cannot be extracted. A low quality image is perceived to have low contrast and poorly defined boundaries between the edges. Most watched images speak to just a corrupted form of the first scene since genuine imaging systems and additionally imaging conditions are normally poor. The principal objective of this study is to manipulate and process an image so that it has a better presentation than the original image. When an image is processed for visual interpretation, the viewer, and the user is the ultimate judge of how well a method works. The visual evaluation of image quality is highly personal to the viewer and the user. The work concentrates on essentially the analysis of the visually enhanced images and a fuzzy approach for further enhancing these images, to make it more readable. The performance of the proposed technique was evaluated in terms of the visual quality, and the stability of the performance of the image enhancement techniques using eight image analysis parameters, to quantify the differences between the original image and the enhanced image. The experiment was carried out to study the performance of the image enhancement schemes and fuzzy logic image at different levels of image defections. The research analyzed this method with PSNR, MSE IQI, SI, and other important metrics in addition to visual comparison. The results then show this method as very good for reducing image defects.


  1. Raman Maini & Himanshu Aggarwal (2010). A Comprehensive Review of Image Enhancement Techniques. Journal of Computing, 2: 8
  2. Niranjan Damera -Venkata, Thomas D. Kite, Wilson S. Geisler, Brian L. Evans, & Alan C. Bovik. (2000). Image Quality Assessment Based on a Degradation Model, IEEE Transactions on Image Processing, 9(4): 636
  3. Pandey A. K., Kavita A., & Mohd. H. (2015). A Hybrid Approach for Enriching Image using Mamdani Neuro- Fuzzy Technique and its Comparative Analysis. International Journal of Computer Applications, Vol. 121
  4. Mahashwari T & Asthana A. (2013). Image Enhancement Using Fuzzy Technique International Journal of Research Review in Engineering Science & Technology, vol. 2,ISSN 2278–6643.
  5. Fullér Robert. (1995). Neural Fuzzy Systems. ISBN 951-650-624-0, ISSN 0358-5654, 208
  6. Vieira José, Fernando Morgado Dias, Alexandre Mota. (2004). Neuro-Fuzzy Systems: A Survey, 5th WSEASNNA International Conference on Neural Networks and Applications, Udine, Italia.
  7. Jan Flusser & Tomáš Suk (1998). Degraded Image Analysis: An Invariant Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 6
  8. Mahashwari T & Asthana A. (2013). Image Enhancement Using Fuzzy Technique International Journal of Research Review in Engineering Science & Technology, vol. 2,ISSN 2278–6643.
  9. Fridman P. (2001). "Radio Astronomy Image Enhancement in the Presence of Phase Errors using Genetic Algorithms," in Int. Conf. on Image Process., Thessaloniki, Greece, pp. 612-615.
  10. Astroart, (2007). "Astroart 4.0,”
  11. Astrostack, 2007. "Astrostack 3.0,”
  12. Armitage D. W. & Oakley J. P. (2003). "Noise Levels in Colour Image Enhancement," in Visual Inform. Eng., London, UK, pp. 105-108.
  13. Buckingham J. M. & Bailey J. (2003). "Imagery Enhancement to Meteorological Collection Platform," in Proc. Syst. And Inform. Eng. Design Symp., Charlottesville, VA, pp. 275- 281.
  14. Woodell G., Jobson D., Rahman Z. U. & Hines G. (2006). "Advanced Image Processing of Aerial Imagery," in Proc. SPIE Visual Inform. Process. XIV, Kissimmee, FL, p. 62460E.
  15. Vorobel R. (1996). "Contrast Enhancement of Remotely-Sensed Images," in 6th Int. Conf. Math. Methods in Electromagnetic Theory, Lviv, Ukraine, pp. 472-475.
  16. Teddy K. (2002)."Fingerprint Enhancement by Spectral Analysis Techniques," in 31st Applied Imagery Pattern Recognition Workshop, Washington DC, WA, pp. 16-18.
  17. Greenberg S., Aladjem M., & Kogan D. (2002). “Fingerprint Image Enhancement using Filtering Techniques,” Real-Time Imaging, vol. 8, no. 3, pp. 227-236.
  18. Z. Abu-Faraj, A. Abdallah, K. Chebaklo, and E. Khoukaz. (2000). “Fingerprint Identification Software for Forensic Applications,” in 7th IEEE Int. Conf. Electronics, Circuits and Systems, Jounieh, Lebanon, pp. 299- 302
  19. Tsai D. Y., Yongbum L., Sekiya M., Sakaguchi S. & Yamada I. (2002). "A Method of Medical Image Enhancement using Wavelet Analysis," in 6th Int. Conf. Signal Process, Beijing, China, pp. 723-726.
  20. Haiguang C. A., Li Kaufman L. & Hale J. (1994). “A Fast Filtering Algorithm for Image Enhancement,” IEEE Trans. Med. Imag., vol. 13, no. 3, pp. 557-564.
  21. Ping W., Li J., Lu D., & Ga C. (2006). "A Multi-scale Enhancement Method to Medical Images Based on Fuzzy Logic," in IEEE Region 10 Conference TENCON, Hong Kong, China, pp. 1-4.
  22. Knee B., W. (2011). Application of improved edge detection algorithm using canny overlapping method Journal of Applied Optics, 4(32), 678-682.
  23. Asadi, B. K., & Amiri J. B. (2015). A review paper: noise models in digital image processing. arXiv preprint arXiv:1505.03489.


Fuzzy logic, Gray Scale Image, Image Enhancement, Similarity Index, Standard Algorithm, Parameters