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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 10 — Apr. 1, 2014
  • pp: B167–B171

Moment domain representation of nonblind image deblurring

Ahlad Kumar, Raveendran Paramesran, and Barmak Honarvar Shakibaei  »View Author Affiliations


Applied Optics, Vol. 53, Issue 10, pp. B167-B171 (2014)
http://dx.doi.org/10.1364/AO.53.00B167


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Abstract

In this paper, we propose the use of geometric moments to the field of nonblind image deblurring. Using the developed relationship of geometric moments for original and blurred images, a mathematical formulation based on the Euler–Lagrange identity and variational techniques is proposed. It uses an iterative procedure to deblur the image in moment domain. The theoretical framework is validated by a set of experiments. A comparative analysis of the results obtained using the spatial and moment domains are evaluated using a quality assessment method known as the Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE). The results show that the proposed method yields a higher quality score when compared with the spatial domain method for the same number of iterations.

© 2014 Optical Society of America

OCIS Codes
(100.1830) Image processing : Deconvolution
(100.2960) Image processing : Image analysis
(100.2980) Image processing : Image enhancement
(100.1455) Image processing : Blind deconvolution

History
Original Manuscript: November 4, 2013
Revised Manuscript: January 30, 2014
Manuscript Accepted: January 30, 2014
Published: March 7, 2014

Citation
Ahlad Kumar, Raveendran Paramesran, and Barmak Honarvar Shakibaei, "Moment domain representation of nonblind image deblurring," Appl. Opt. 53, B167-B171 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-10-B167


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References

  1. J. Chen, W. Dong, H. Feng, Z. Xu, and Q. Li, “High quality non-blind image deconvolution using the fields of experts prior,” Optik 124, 3601–3606 (2013). [CrossRef]
  2. M. Almeida and M. Figueiredo, “Parameter estimation for blind and non-blind deblurring using residual whiteness measures,” IEEE Trans. Image Process. 22, 2751–2763 (2013). [CrossRef]
  3. S. Tao, W. Dong, H. Feng, Z. Xu, and Q. Li, “Non-blind image deconvolution using natural image gradient prior,” Optik 124, 6599–6605 (2013). [CrossRef]
  4. S. Tang, W. Gong, W. Li, and W. Wang, “Non-blind image deblurring method by local and nonlocal total variation models,” Signal Process. 94, 339–349 (2014). [CrossRef]
  5. E. Vera, M. Vega, R. Molina, and A. K. Katsaggelos, “Iterative image restoration using nonstationary priors,” Appl. Opt. 52, D-102–D-110 (2013). [CrossRef]
  6. D. S. Stoker, J. Wedd, E. Lavelle, and J. van der Laan, “Restoration and recognition of distant, blurry irises,” Appl. Opt. 52, 1864–1875 (2013). [CrossRef]
  7. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, “Removing camera shake from a single photograph,” ACM Trans. Graph. 25, 787–794 (2006). [CrossRef]
  8. M. Ben-Ezra and S. Nayar, “Motion deblurring using hybrid imaging,” in Computer Vision and Pattern Recognition (IEEE, 2003), Vol. 1, pp. I-657–I-664.
  9. L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, “Image deblurring with blurred/noisy image pairs,” ACM Trans. Graph. 26, 1–10 (2007). [CrossRef]
  10. R. Raskar, A. Agrawal, and J. Tumblin, “Coded exposure photography: motion deblurring using fluttered shutter,” ACM Trans. Graph. 25, 795–804 (2006). [CrossRef]
  11. A. Levin, “Blind motion deblurring using image statistics,” in Advances in Neural Information Processing Systems, B. Schölkopf, J. Platt, and T. Hofmann, eds. (MIT, 2006), pp. 841–848.
  12. J. Jia, “Single image motion deblurring using transparency,” in Computer Vision and Pattern Recognition Conference (IEEE, 2007), pp. 1–8.
  13. T. Chan and J. Shen, Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods (Society for Industrial and Applied Mathematics, 2005).
  14. J. Flusser, B. Zitova, and T. Suk, Moments and Moment Invariants in Pattern Recognition (Wiley, 2009).
  15. R. Mukundan, S. Ong, and A. Lee, “Image analysis by Tchebichef moments,” IEEE Trans. Image Process. 10, 1357–1364 (2001). [CrossRef]
  16. P.-T. Yap, R. Paramesran, and S.-H. Ong, “Image analysis by Krawtchouk moments,” IEEE Trans. Image Process. 12, 1367–1377 (2003). [CrossRef]
  17. A. Mittal, A. Moorthy, and A. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process. 21, 4695–4708 (2012). [CrossRef]
  18. http://classes.soe.ucsc.edu/ee264/Fall11/LecturePDF/5-LocalOperations.pdf .

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