We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

HSI Image Restortion using Low Rank Matrix Recovery

by Alisha Chug, Shashi Bhushan, Karan Mahajan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 6
Year of Publication: 2015
Authors: Alisha Chug, Shashi Bhushan, Karan Mahajan
10.5120/20562-2951

Alisha Chug, Shashi Bhushan, Karan Mahajan . HSI Image Restortion using Low Rank Matrix Recovery. International Journal of Computer Applications. 117, 6 ( May 2015), 34-36. DOI=10.5120/20562-2951

@article{ 10.5120/20562-2951,
author = { Alisha Chug, Shashi Bhushan, Karan Mahajan },
title = { HSI Image Restortion using Low Rank Matrix Recovery },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 6 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number6/20562-2951/ },
doi = { 10.5120/20562-2951 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:58:39.447817+05:30
%A Alisha Chug
%A Shashi Bhushan
%A Karan Mahajan
%T HSI Image Restortion using Low Rank Matrix Recovery
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 6
%P 34-36
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Restoration is process of undoing or recovering an image from degraded state. The image restoration techniques are used to make the corrupted image as similar as that of the original image. Hyper spectral image are often corrupted by a mixture of various kinds of noise in the acquisition process, which can include Gaussian noise, impulse noise, deadlines, stripes, and so on. Various restoration methods are used. A new HSI restoration method based on low-rank matrix recovery (LRMR) is introduced, which can simultaneously remove the Gaussian noise, impulse noise, deadlines, and stripes but there is no spatial constraint applied on neighbouring pixels that causes large areas of missing pixels . To handle the problem of missing pixels non-reference regularization algorithm is used .

References
  1. Hongyan Zhang,Member IEEE ,"Hyperspectral Images Restoration Using Low-Rank Matrix Recovery",Ieee Traction On Geoscience And Remote Sensing , Vol. 52,No. 8,August 2014.
  2. D. Letexier and S. Bourennane , "Noise removal from hyperspectral images by multidimensional filtering," IEEE Trans. Geosci. Remote Sens. , vol. 46, no. 7, pp. 2061–2069, Jul. 2008.
  3. X. Liu, S. Bourennane, and C. Fossati, "Nonwhite noise reduction in hyperspectral images," IEEE Geosci. Remote Sens. Lett. , vol. 9, no. 3, pp. 368–372, May 2012.
  4. J. Wright, A. Ganesh, S. Rao, Y. Peng, and Y. Ma, "Robust principal component analysis: Exact recovery of corrupted low-rank matrices viaconvex optimization," in Proc. NIPS, 2009, pp. 2080–2088.
  5. B. Recht, M. Fazel, and P. A. Parrilo, "Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization," J. SIAM Rev. , vol. 52, no. 3, pp. 471–501, Aug. 2010.
  6. E. J. Candes and T. Tao, "The power of convex relaxation: Near-optimal matrix completion," IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2053–2080, May 2010.
  7. J. -F. Cai, E. J. Candès, and Z. Shen, "A singular value thresholding algorithm for matrix completion," SIAM J. Optim. , vol. 20, no. 4, pp. 1956–
  8. 1982, Jan. 2010.
  9. E. J. Candes, X. Li, Y. Ma, and J. Wright, "Robust principal component analysis" J. ACM, vol. 58, no. 3, May 2011.
  10. Chao Zeng , Huanfeng Shen , Liangpei Zhang," Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method" Remote Sensing of Environment 131 (2013) 182–194
  11. Er. Priya Tiwari, Dr. Naveen Dhillon and Er. Kuldeep Sharma,Tzikas, D. G. , Likas, A. C. , Galatsanos, N. P, "Variational Bayesian Blind Image Deconvolution with Student-T Priors," IEEE, Image Processing, pp. 109-112, 2007.
  12. Zhang X. F, Ye H, Tian W. F, Chen W. F, "Denoising DWI Based on Regularized Filter," IEEE, pp. 120-121, 2007.
  13. Oleg Michailovich, Allen Tannenbaum, "Blind Deconvolution of Medical Ultrasound Images:A Parametric Inverse Filtering Approach , " IEEE, Image processing, pp. 3005-3019,2007.
  14. Mateos, J. , Bishop, T. E. , Molina, R. , Katsaggelos, A. K. , "Local Bayesian image restoration using variational methods and Gamma-Normal distributions," IEEE, Image Processing (ICIP), pp. 129 – 132, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Image Restoration HSI Low Rank Matrix Recovery (LRMR)