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A Hybrid Method for Blur Invariants in Images using Contourlet Transforms

by S. Keerthiga, M. Meenakumari, Shalakha Rajan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 22
Year of Publication: 2013
Authors: S. Keerthiga, M. Meenakumari, Shalakha Rajan
10.5120/11246-5858

S. Keerthiga, M. Meenakumari, Shalakha Rajan . A Hybrid Method for Blur Invariants in Images using Contourlet Transforms. International Journal of Computer Applications. 66, 22 ( March 2013), 8-17. DOI=10.5120/11246-5858

@article{ 10.5120/11246-5858,
author = { S. Keerthiga, M. Meenakumari, Shalakha Rajan },
title = { A Hybrid Method for Blur Invariants in Images using Contourlet Transforms },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 22 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number22/11246-5858/ },
doi = { 10.5120/11246-5858 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:23:05.881810+05:30
%A S. Keerthiga
%A M. Meenakumari
%A Shalakha Rajan
%T A Hybrid Method for Blur Invariants in Images using Contourlet Transforms
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 22
%P 8-17
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Radiometric degradation is a common problem in the image restoration part of many applications. The degradation may involve blurring, information loss due to sampling, quantization effects and various sources of noise. The purpose of image restoration is to estimate the original image from the degraded image. There is much research carried out in an effort to deblur such images. To tackle this problem, different blur invariants had existed so far. Wavelet domain blurs invariants are used only for discrete 2D signals in spatial domain where it is concentrated in centrally symmetric blurs and also in the wavelet domain, directional prediction is so hard to find smoother contours. In this paper, the Contourlet Transform was proposed to address the lack of geometrical structure in the separable 2D Wavelet Transform. Because of its filter bank structure, the Contourlet Transform is not Shift invariant. Contourlet not only possess the features of wavelet (namely multiscale and time frequency localization), but also offer a high degree of directionality and anisotropy. The Contourlet domain invariant is proposed for both 2D and 3D signals based on frequency domain. By using disc and motion filter, the blur images are produced and are divided into equal pixels and also the dependent terms are discarded in blur invariants which reduce correlation, simplifies computation and also reduces noise using a Wiener filter in order to get the deblurred image. It is also proved that frequency domain blur invariants are a special version of the proposed invariants and numerical experiments on an image deblurring show that the proposed new Contourlet Transform can significantly outperform in terms of PSNR (by several DB's). It is widely used in various fields of applications, such as medical imaging, astronomical imaging, remote sensing, microscopy imaging, photography imaging, photography deblurring and forensic

References
  1. Iman Makaremi and Majid Ahmadi, "Wavelet-Domain Blur Invariants For Image Analysis" IEEE Trans. In image processing, Vol. 21, No. 3, March 2012.
  2. M. -K. Hu, "Visual pattern recognition by moment invariants," IRE Trans. Inf. Theory, vol. 8, no. 2, pp. 179–187, Feb. 1962.
  3. J. Flusser, J. Kautsky, and F. Roubek, "Implicitmoment invariants," Int. J. Comput. Vis. , vol. 86, no. 1, pp. 72–86, Jan. 2010.
  4. T. H. Reiss, Recognizing Planar Objects Using Invariant Image Features. Secaucus, NJ: Springer-Verlag, 1993.
  5. J. Wood, "Invariant pattern recognition: A review," Pattern Recognit. , vol. 29, no. 1, pp. 1–17, Jan. 1996.
  6. J. Flusser, T. Suk, and B. Zitov, Moments and Moment Invariants in Pattern Recognition. Hoboken, NJ: Wiley, 2009.
  7. M. I. Sezan and A. M. Tekalp, "Survey of recent developments in digital image restoration," Opt. Eng. , vol. 29, no. 5, pp. 393–404, May 1990.
  8. D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Signal Process. Mag. , vol. 13, no. 3, pp. 43–64, May 1996.
  9. B. Gunturk, A. Batur, Y. Altunbasak, M. H. Hayes, III, and R. Mersereau, "Eigenface-domain super-resolution for face recognition," IEEE Trans. Image Process. , vol. 12, no. 5, pp. 597–606, May 2003.
  10. J. Flusser, T. Suk, and S. Saic, "Image features invariant with respect to blur," Pattern Recognit. , vol. 28, no. 11, pp. 1723–1732, Nov. 1995.
  11. J. Flusser, T. Suk, and S. Saic, "Recognition of blurred images by the method of moments," IEEE Trans. Image Process. , vol. 5, no. 3, pp. 533–538, Mar. 1996.
  12. J. Flusser and T. Suk, "Degraded image analysis: An invariant approach," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 20, no. 6, pp. 90–603, Jun. 1998.
  13. T. Suk and J. Flusser, "Features invariant simultaneously to convolution and affine transformation," in Proc. CAIP, W. Skarbek, Ed. , 2001, vol. 2124, Lecture Notes in Computer Science, pp. 183–190.
  14. J. Flusser and T. Suk, "Classification of degraded signals by the method of invariants," Signal Process. , vol. 60, no. 2, pp. 243–249, Jul. 1997.
  15. J. Flusser and B. Zitová, "Combined invariants to linear filtering and rotation," Int. J. Pattern Recognit. Artif. Intell. , vol. 13, no. 8, pp. 1123–1136, Dec. 1999.
  16. J. Liu and T. Zhang, "Recognition of the blurred image by complex moment invariants," Pattern Recognit. Lett. , vol. 26, no. 8, pp. 1128–1138, Jun. 2005.
  17. S. Metari and F. Deschenes, "New classes of radiometric and combined radiometric-geometric invariant descriptors," IEEE Trans. Image Process. , vol. 17, no. 6, pp. 991–1006, Jun. 2008.
  18. H. Zhang, H. Shu, G. Han, G. Coatrieux, L. Luo, and J. Coatrieux, "Blurred image recognition by Legendre moment invariants," IEEE Trans. Image Process. , vol. 19, no. 3, pp. 596–611, Mar. 2010.
  19. H. Ji and H. Zhu, "Degraded image analysis using Zernike moment invariants," in Proc. IEEE ICASSP, Apr. 2009, pp. 1941–1944.
  20. J. Flusser, J. Boldys, and B. Zitova, "Moment forms invariant to rotation and blur in arbitrary number of dimensions," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 25, no. 2, pp. 234–246, Feb. 2003.
  21. V. Ojansivu and J. Heikkilä, "Object recognition using frequency domain blur invariant features," in Proc. SCIA, B. K. Ersbll and K. S. Pedersen, Eds. , 2007, vol. 4522, Lecture Notes in Computer Science, pp. 243–252.
  22. V. Ojansivu and J. Heikkilä, "A method for blur and similarity transform invariant object recognition," in Proc. ICIAP, R. Cucchiara, Ed. , 2007, pp. 583–588.
  23. V. Ojansivu and J. Heikkila, "Image registration using blur-invariant phase correlation," IEEE Signal Process. Lett. , vol. 14, no. 7, pp. 449–452, Jul. 2007.
  24. Y. Bentoutou, N. Taleb, M. C. E. Mezouar, M. Taleb, and L. Jetto, "An invariant approach for image registration in digital subtraction angiography," Pattern Recognit. , vol. 35, no. 12, pp. 2853–2865, 2002.
  25. Y. Bentoutou, N. Taleb, K. Kpalma, and J. Ronsin, "An automatic image registration for applications in remote sensing," IEEE Trans. Geosci. Remote Sens. , vol. 43, no. 9, pp. 2127–2137, Sep. 2005.
  26. B. Mahdian and S. Saic, "Detection of copy-move forgery using a method based on blur moment invariants," Forensic Sci. Int. , vol. 171, no. 2/3, pp. 180–189, Sep. 2007.
  27. X. Dai, H. Zhang, H. Shu, and L. Luo, "Image recognition by combined invariants of legendre moment," in Proc. IEEE ICIA, 2010, pp. 1793–1798.
  28. M. Pedone and J. Heikkilä, "Blur and contrast invariant fast stereo matching," in Advanced Concepts for Intelligent Vision Systems, J. Blanc-Talon, S. Bourennane, W. Philips, D. C. Popescu, and P. Scheunders, Eds. Berlin, Germany: Springer, 2008.
  29. Y. Bentoutou and N. Taleb, "Automatic extraction of control points for digital subtraction angiography image enhancement," IEEE Trans. Nucl. Sci. , vol. 52, no. 1, pp. 238–246, Feb. 2005.
  30. P. A. Mlsna and J. J. Rodriguez, "Gradient and laplacian edge detection," in Handbook of Image and Video Processing, 2nd ed. Elsevier Academic Press, 2005, ch. 4. 13.
  31. S. G. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation," IEEE Transations on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, July 1989.
  32. J. Antoine, P. Carrette, R. Murenzi, and B. Piette, "Image analysis with two-dimensional continuous wavelet transform," Signal Processing, vol. 31, pp. 241–272, 1993.
  33. M. N. Do and M. Vetterli, "The contourlet transform: An efficient directional multiresolution image representation," IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2091–2096, Dec 2005.
  34. J. -L. Starck, E. J. Candes, and D. L. Donoho, "The curvelet transform for image denoising," IEEE Transactions on Image Processing, vol. 11, no. 6, pp. 2091–2096, Jun 2002.
  35. M. N. Do, "Contourlet toolbox. " [Online]. Available: http://www. ifp. uiuc. edu/minhdo/software/contourlettoolbox. tar
Index Terms

Computer Science
Information Sciences

Keywords

Blur moment invariants Centrally Symmetric blur Contourlet domain invariants shift-invariant wavelet transform

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