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Image Data Denoising using Center Pixel Weights in Non-Local Means and Smart Patch-based, Modern Machine Learning Method using Higher Order Singular Value Decomposition: A Review

by Jeetesh Kumar Rajak, Achint Chugh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 14
Year of Publication: 2015
Authors: Jeetesh Kumar Rajak, Achint Chugh
10.5120/20221-2501

Jeetesh Kumar Rajak, Achint Chugh . Image Data Denoising using Center Pixel Weights in Non-Local Means and Smart Patch-based, Modern Machine Learning Method using Higher Order Singular Value Decomposition: A Review. International Journal of Computer Applications. 115, 14 ( April 2015), 22-25. DOI=10.5120/20221-2501

@article{ 10.5120/20221-2501,
author = { Jeetesh Kumar Rajak, Achint Chugh },
title = { Image Data Denoising using Center Pixel Weights in Non-Local Means and Smart Patch-based, Modern Machine Learning Method using Higher Order Singular Value Decomposition: A Review },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 14 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number14/20221-2501/ },
doi = { 10.5120/20221-2501 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:54:50.157883+05:30
%A Jeetesh Kumar Rajak
%A Achint Chugh
%T Image Data Denoising using Center Pixel Weights in Non-Local Means and Smart Patch-based, Modern Machine Learning Method using Higher Order Singular Value Decomposition: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 14
%P 22-25
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this work, there is a comparison related to image denoising techniques between center pixel weights (CPW) in Non-Local Means (NLM) and smart patch-based, modern technique using the higher order singular value decomposition (HOSVD). The HOSVD technique simply compose in a cluster, alike Patches of noisy image in 3D heap, work out HOSVD factors of this heap, handles these factors by stiff thresholding, and turn upside down the HOSVD transmute to yield the final resultant image. Whereas (NLM) and its variants have proven to be effective and robust in many image denoising tasks. It is experimentally demonstrating approximately 12 percent improved PSNR characteristics of HOSVD technique on gray scale images. The HOSVD process yields state-of-the-art outcomes on gray images, than the center pixel weights (CPW) in NLM image data denoising process at moderately great noise stages.

References
  1. Buades, B. Coll, and J. Morel, "A review of image denoising algorithms, with a new one," Multiscale Model. Simul. , vol. 4, no. 2, pp. 490–530, 2005.
  2. Buades, B. Coll, and J. -M. Morel, "A non-local algorithm for image denoises," in IEEE Compu. Soc. Conf. Computer Vision and Pattern Recognition, Jun. 2005, vol. 2, pp. 60–65, vol. 2.
  3. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3-d transform-domain collaborative filtering," IEEE Trans. Image Process. , vol. 16, no. 8, pp. 2080–2095, 2007
  4. Deledalle, V. Duval, and J. Salmon, "Non-local methods with sha- peadaptive patches (nlm-sap)," J. Math. Imag. Vis. , vol. 43, no. 2, pp. 103–120, 2012.
  5. V. Duval, J. Aujol, and Y. Gousseau, "A bias-variance approach for the nonlocal means," SIAM J. Imag. Sci. , vol. 4, no. 2, pp. 760–788, 2011.
  6. J. Salmon, "On two parameters for denoising with non-local means," IEEE Signal Process. Lett. , vol. 17, no. 3, pp. 269–272, Mar. 2010.
  7. J. Darbon, A. Cunha, T. Chan, S. Osher, and G. Jensen, "Fast non- local filtering applied to electron cryomicroscopy," in IEEE Int. Symp. Biomedical Imaging: From Nano to Macro, 2008, pp. 1331–1334.
  8. R. Vignesh, B. T. Oh, and C. -C. Kuo, "Fast non-local means (nlm) com- putation with probabilistic early termination," IEEE Signal Process. Lett. vol. 17, no. 3, pp. 277–280, Mar. 2010.
  9. W. Zeng and X. Lu, "Region-based non-local means algorithm for noise removal," Electron. Lett. vol. 47, no. 20, pp. 1125–1127, 2011.
  10. W. James and C. Stein, "Estimation with quadratic loss," in Proc. Fourth Berkeley Symp. Mathematical Statistics and Probability, 1961, vol. 1, no. 36, pp. 1–379.
  11. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Trans. Image Process. , vol. 13, no. 4, pp. 600–612, 2004.
  12. L. de Lathauwer, "Signal Processing Based on Multilinear Algebra," PhD dissertation, KatholiekeUniversiteit Leuven, Belgium, 1997
  13. Donoho and I. Johnstone, "Ideal Spatial Adaptation by Wavelet Shrinkage," Biometrika, vol. 81, pp. 425-455, 1993.
  14. L. Zhang, W. Dong, D. Zhang and G. Shi, "Two-Stage Image Denoising by Principal Component Analysis with Local Pixel Grouping," Pattern Recognition, vol. 43, no. 4, pp. 1531-1549, 2010.
  15. AjitRajwade, AnandRangarajan and Arunava Banerjee, "Image Denoising Using the Higher Order Singular Value Decomposition" IEEE Transactions On Pattern Analysis and Machine Intelligence, Vol. 35, NO. 4, APRIL 2013.
  16. Yue Wu, Brian Tracey, PremkumarNatarajan, and Joseph P. Noonan James–Stein Type Center Pixel Weights for Non-Local Means Image Denoising IEEE Signal Processing Letters, Vol. 20, No. 4, April 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Image data denoising singular value decomposition (SVD) HOSVD patch Basis similarity

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