Rajpoot, Nasir M. (Nasir Mahmood) and Butt, Irfan T.. (2012) A multiresolution framework for local similarity based image denoising. Pattern Recognition, Vol.45 (No.8). pp. 2938-2951. ISSN 0031-3203

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Abstract

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Image processing -- Digital techniques, Digital images -- Editing, Digital filters (Mathematics)
Journal or Publication Title:	Pattern Recognition
Publisher:	Pergamon
ISSN:	0031-3203
Date:	2012
Volume:	Vol.45
Number:	No.8
Page Range:	pp. 2938-2951
Identification Number:	10.1016/j.patcog.2012.01.023
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
References:	[1] P. Perona, J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelli- gence 12 (7) (1990) 629–639. [2] Z. Farbman, R. Fattal, D. Lischinski, R. Szeliski, Edge-preserving de- compositions for multi-scale tone and detail manipulation, Proceedings of ACM SIGGRAPH 27 (3) (2008) 67:1–67:10. [3] O. Scherzer, J. Weickert, Relations between regularization and diffusion filtering, Journal of Mathematical Imaging and Vision 12 (1) (2000) 43–63. [4] M. Black, G. Sapiro, D. Marimont, D. Heeger, Robust anisotropic dif- fusion, IEEE Transactions on Image Processing 7 (3) (1998) 421–432. [5] D. Donoho, J. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika 81 (3) (1994) 425–455. [6] D. Donoho, X. Huo, Combined image representation using edgelets and wavelets, in: Proceedings of SPIE, Vol. 3813, 1999, pp. 468–476. [7] C. Jung, J. Scharcanski, Adaptive image denoising and edge enhance- ment in scale-space using the wavelet transform, Pattern Recognition Letters 24 (7) (2003) 965–971. [8] Z. Hou, Adaptive singular value decomposition in wavelet domain for image denoising, Pattern Recognition 36 (8) (2003) 1747–1763. [9] E. Candes, Ridgelets: theory and applications, Ph.D. thesis, Standford University (1998). [10] J. Starck, E. Cand`es, D. Donoho, The curvelet transform for image denoising, IEEE Transactions on image processing 11 (6) (2002) 670– 684. [11] M. Do, M. Vetterli, The finite ridgelet transform for image representa- tion, IEEE Transactions on Image Processing 12 (1) (2003) 16–28. [12] M. Do, M. Vetterli, The contourlet transform: an efficient directional multiresolution image representation, IEEE Transactions on image pro- cessing 14 (12) (2005) 2091–2106. [13] Y. Lu, M. Do, A new contourlet transform with sharp frequency local- ization, in: IEEE International Conference on Image Processing, 2006, pp. 1629–1632. [14] G. Chen, B. K´egl, Image denoising with complex ridgelets, Pattern Recognition 40 (2) (2007) 578–585. [15] X. Wang, Wrap-around effect removal finite ridgelet transform for mul- tiscale image denoising, Pattern Recognition 43 (11) (2010) 3693–3698. [16] F. Meyer, R. Coifman, Brushlets: a tool for directional image analysis and image compression, Applied and computational harmonic analysis 4 (2) (1997) 147–187. [17] Z. Yao, N. Rajpoot, Image denoising using multiscale directional co- sine bases, in: Proceedings IEEE International Conference on Image Processing (ICIP), Vol. 3, 2005, pp. 313–316. [18] M. Aharon, M. Elad, A. Bruckstein, K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Transactions on signal processing 54 (11) (2006) 4311–4322. [19] L. Demanet, L. Ying, Wave atoms and sparsity of oscillatory patterns, Applied and Computational Harmonic Analysis 23 (3) (2007) 368–387. [20] N. Rajpoot, R.Wilson, Z. Yao, Planelets: A new analysis tool for planar feature extraction, in: Proceedings 5th International Workshop on Im- age Analysis for Multimedia Interactive Services (WIAMIS’2004), 2004. [21] A. Buades, B. Coll, J. Morel, A non-local algorithm for image denois- ing, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, 2005, pp. 60–65. [22] M. Mahmoudi, G. Sapiro, Fast image and video denoising via nonlocal means of similar neighborhoods, IEEE Signal Processing Letters 12 (12) (2005) 839–842. [23] T. Brox, O. Kleinschmidt, D. Cremers, Efficient nonlocal means for denoising of textural patterns, IEEE Transactions on Image Processing 17 (7) (2008) 1083–1092. [24] T. Tasdizen, Principal neighborhood dictionaries for nonlocal means im- age denoising, IEEE Transactions on Image Processing 18 (12) (2009) 2649–2660. [25] D. van de Ville, M. Kocher, SURE-based non-local means, IEEE Signal Processing Letters 16 (11) (2009) 973–976. [26] A. Foi, V. Katkovnik, K. Egiazarian, Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images, IEEE Transactions on Image Processing 16 (5) (2007) 1395–1411. [27] K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3-d transform-domain collaborative filtering, IEEE Transactions on Image Processing 16 (8) (2007) 2080–2095. [28] V. Katkovnik, A. Foi, K. Egiazarian, J. Astola, From local kernel to non local multiple-model image denoising, International Journal of Com- puter Vision 86 (1) (2009) 1–32. [29] C. Tomasi, R. Manduchi, Bilateral filtering for gray and color images, in: International Conference on Computer Vision (ICCV), 1998, pp. 839–846. [30] D. Barash, Fundamental relationship between bilateral filtering, adap- tive smoothing, and the nonlinear diffusion equation, IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (6) (2002) 844–847. [31] R. Garnett, T. Huegerich, C. Chui, W. He, A universal noise removal algorithm with an impulse detector, IEEE Transactions on Image Pro- cessing 14 (11) (2005) 1747–1754. [32] P. Choudhury, J. Tumblin, The trilateral filter for high contrast images and meshes, in: Proceedings of the 14th Eurographics workshop on Ren- dering, Eurographics Association Aire-la-Ville, Switzerland, Switzer- land, 2003, pp. 186–196. [33] Z. Yu, Z.-K. Shi, R.-Q. Wang, A multilateral filtering method applied to airplane runway image, arxiv.org (May 2008). [34] E. Simoncelli, W. Freeman, The steerable pyramid: A flexible archi- tecture for multi-scale derivative computation, in: Proceedings Interna- tional Conference on Image Processing (ICIP), Vol. 3, 1995, pp. 444–447. [35] M. Zhang, B. Gunturk, Multiresolution bilateral filtering for image de- noising, IEEE Transactions on Image Processing 17 (12) (2008) 2324– 2333. [36] H. Yu, L. Zhao, H. Wang, Image denoising using trivariate shrinkage fil- ter in the wavelet domain and joint bilateral filter in the spatial domain, IEEE Transactions on Image Processing 18 (10) (2009) 2364–2369. [37] S. Chang, B. Yu, M. Vetterli, Adaptive wavelet thresholding for im- age denoising and compression, IEEE Transactions on Image Processing 9 (9) (2000) 1532–1546. [38] S. Mallat, Wavelets for a vision, Proceedings of the IEEE 84 (4) (1996) 604–614. [39] A. Bosch, A. Zisserman, X. Munoz, Representing shape with a spatial pyramid kernel, in: Proceedings of the 6th ACM international conference on Image and video retrieval, ACM New York, NY, USA, 2007, pp. 401– 408. [40] P. Burt, E. Adelson, The Laplacian pyramid as a compact image code, IEEE Transactions on Communications 31 (4) (1983) 532–540. [41] N. Rajpoot, Z. Yao, R. Wilson, Adaptive wavelet restoration of noisy video sequences, in: Proceedings IEEE International Conference on Im- age Processing (ICIP), 2004, pp. 957–960. [42] N. Otsu, A threshold selection method from gray-level histograms, Au- tomatica 11 (1975) 285–296. [43] J. Xie, P. Heng, M. Shah, Image diffusion using saliency bilateral filter, IEEE Transactions on Information Technology in Biomedicine 12 (6) (2008) 768–771. [44] L. Villemoes, Wavelet packets with uniform time-frequency localization, Comptes rendus-Math´ematique 335 (10) (2002) 793–796.
URI:	http://wrap.warwick.ac.uk/id/eprint/48852

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