Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Adaptive Image Quality Enhancement with Hybrid Pixel Enhancement Approach

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Sukhwinder Singh, Amit Grover
10.5120/ijca2016909858

Sukhwinder Singh and Amit Grover. Adaptive Image Quality Enhancement with Hybrid Pixel Enhancement Approach. International Journal of Computer Applications 142(7):7-11, May 2016. BibTeX

@article{10.5120/ijca2016909858,
	author = {Sukhwinder Singh and Amit Grover},
	title = {Adaptive Image Quality Enhancement with Hybrid Pixel Enhancement Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {142},
	number = {7},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {7-11},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume142/number7/24906-2016909858},
	doi = {10.5120/ijca2016909858},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Restoration is procedure of recoupling a picture from corrupted state. The image enhancement and restoration requires the image matrix processing, which utilizes the noise removal and contrast enhancement. The image enhancement method in the proposed model usually utilize the color enhancement, histogram equalization, color illumination, neighbor based reference model, non-reference image matrix enhancement and co-variance based matrix enhancement. In this paper, the combination of the neighbor based reference model and non-reference image matrix enhancement is proposed for the enhancement of the results. The experimental results have been performed over the grayscale standard images of the Lena, Baboon, Barbara and Peppers.

References

  1. A.Gotchev, J. Vesma, T. Saramaki and K. Egiazarian," Digital image re sampling by modified B-spline functions,"IEEE Nordic Signal Processing symposium, pp. 259-262, June 2000.
  2. A. M. Darwish, M. S. Bedair and S.I. Shaheen, “ Adaptive resampling algorithm for image enhancement” , IEEE Proc. Vis. Image Signal Process, vol. 144, No. 4, August 1997.
  3. AnderaGiachetti and Nicola Asuni, “ Real-Time artifact free Image up scaling”, IEEE Transactions on Image Processing, vol. 20 No. 10, October 2011.
  4. B. S. Morse and D. Schwartzwald,"Image magnification using level-set reconstruction", Proc. IEEE Int. Cof. Computer Vision Pattern Recognition, vol.3, pp. 333-340, 2001.
  5. Buades, Antoni, BartomeuColl, and Jean-Michel Morel. "Nonlocal image and movie denoising." International journal of computer vision 76.2, pp. 123-139, 2008
  6. C. B. Atkins, C. A. Bouman and J. P. Allebach, “Optimal image scaling using pixel classification”, Proc. IEEE Int. Conf. Image Processing, vol. 3, pp. 864-867, 2001.
  7. Concus, P., Golub, G. H., & O'Leary, D. P. A generalized conjugate gradient method for the numerical: Solution of elliptic partial differential equations: Computer Science Department, School of Humanities and Sciences. : Stanford University, 1976
  8. Gonzalez, Rafael C. "RE woods, Digital Image Processing." Addison–Wesely Publishing Company (1992).
  9. H.chen and G. E. Ford,"An FIR enhancement filter Design method based on properties of HumanVision", Proc. IEEE Int. Conf. Image Processing, vol.3, PP.581-585, November 1994.
  10. H.Jiang and C. Moloney,"A new direction adaptive scheme for image enhancement", Proc. IEEE Int. Conf. Image Processing, vol.3, pp.369-372, 2002. ) Tzikas, Dimitris G., Aristidis C. Likas, and Nickolaos P. Galatsanos. "The variational approximation for Bayesian inference." Signal Processing Magazine, IEEE 25.6 (2008): 131-146.
  11. Zhang X. F, Ye H, Tian W.F, Chen W.F, “Denoising DWI Based on Regularized Filter,” IEEE, pp. 120-121,10-11march 2007.
  12. Mateos, J., Bishop, T.E., Molina, R., Katsaggelos, A.K.,“Local Bayesian image restoration using variational methods and Gamma-Normal distributions,” IEEE, Image Processing (ICIP), pp.129, 132, 7-10, nov2009.

Keywords

Image enhancement, contrast enhancement, noise elimination, contrast adjustment.