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Reseach Article

An Adaptive and High Quality Blind Image Deblurring using Spectral Properties

by Seethu George, Resmi Cherian
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 130 - Number 13
Year of Publication: 2015
Authors: Seethu George, Resmi Cherian
10.5120/ijca2015907159

Seethu George, Resmi Cherian . An Adaptive and High Quality Blind Image Deblurring using Spectral Properties. International Journal of Computer Applications. 130, 13 ( November 2015), 33-40. DOI=10.5120/ijca2015907159

@article{ 10.5120/ijca2015907159,
author = { Seethu George, Resmi Cherian },
title = { An Adaptive and High Quality Blind Image Deblurring using Spectral Properties },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 130 },
number = { 13 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 33-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume130/number13/23271-2015907159/ },
doi = { 10.5120/ijca2015907159 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:25:28.920482+05:30
%A Seethu George
%A Resmi Cherian
%T An Adaptive and High Quality Blind Image Deblurring using Spectral Properties
%J International Journal of Computer Applications
%@ 0975-8887
%V 130
%N 13
%P 33-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Blurring is a common artifact that produces distorted images with unavoidable information loss. The Blind image deconvolution is to recover the sharp estimate of a given blurry image when the blur kernel is unknown. Despite the availability of deconvolution methods, it is still uncertain how to regularize the blur kernel in an effectual fashion which could substantially improve the results even when the image is blurred to its extend. This paper presents a novel deconvolution method that describes an efficient optimization scheme that alternates between estimation of blur kernel and restoration of sharp image until convergence. The system engenders a more efficient regularizer for the blur kernel that can generally and considerably benefit the solution for the problem of blind deconvolution. Also the blur metric concept in the system provides an automated environment for the selection of deconvolutoin parameters. The outlier handling model used in this work detects and eliminates the major causes of visual artifacts. As a result the system produces high quality deblurred results that preserves fine edge details of an image and complex image structures, while avoiding visual artifacts. The experiments on realistic images show that the proposed deconvolution method can produce high quality deblurred images with very little ringing artifacts even when the image is severely blurred, and the ability of system in choosing the appropriate input parameters for deconvolution.

References
  1. Guangcan Liu, Member, IEEE, Shiyu Chang, and, Fellow, IEEE, “Blind Image Deblurring Using Spectral Properties of Convolution Operators," IEEE Transactions on image processing, Vol. 23, NO. 12, December 2014.
  2. D. Krishnan and R. Fergus, “Fast image deconvolution using hyper-Laplacian priors," in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2009, pp. 10331041.
  3. Frederique Crete-Roffet, Thierry Dolmiere, Patricia Ladret, Marina Nicolas.“The Blur Effect: Perception and Estimation with a New No-Reference Perceptual Blur Metric," SPIE Electronic Imaging Symposium Conf Human Vision and Electronic Imaging, - GRENOBLE – 2007
  4. S. Cho, J. Wang, and S. Lee, “Handling outliers in non-blind image deconvolution," in Proc. Int. Conf. Comput. Vis., 2011, pp. 495502.
  5. Q. Shan, J. Jia, and A. Agarwala, “High-quality motion deblurring from a single image," ACM Trans. Graph., vol. 27, no. 3, p. 73, 2008
  6. S. Cho and S. Lee, “Fast motion deblurring," ACM Trans. Graph., vol. 28, no. 5, p. 145, 2009.
  7. L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, “Progressive inter-scale and intra-scale non-blind image deconvolution," ACM Trans. Graph., vol. 27, no. 3, pp. 110, 2008.
  8. Mariana S., C. Almeida and Lus B. Almeida, “Blind and Semi-Blind Deblurring of Natural Images," IEEE Transactions on image processing, Vol. 19, NO. 1, January 2010.
  9. Jinshan Pan and Zhixun Su, “Fast ℓ0 Regularized Kernel Estimation for Robust Motion Deblurring," IEEE signal processing, Vol. 20, NO. 9, September 2013.
  10. J. Jia, “Single image motion deblurring using transparency,” in Proc.IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2007, pp. 1–8.
  11. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman,“Removing camera shake from a single photograph,” ACM Trans.Graph., vol. 25, no. 3, pp. 787–794, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Image deblurring blind deconvolution blur kernel estimation point spread function spectral methods outlier detection blur metric.