Performance of the low rank matrix technique in image de-noising
About this article
DOI:
https://doi.org/10.14419/ijet.v7i4.17569Keywords:
Low Rank Matrix Method, Denoising, PSNR.Abstract
Generally, the technique of low rank matrix (LRM) estimation is a very handy tool in signal processing. The same can be further extended to two dimensional problems in image processing. The tool also emerged as a favourable method to provide solutions through the machine learning and other statistical techniques. In this paper, an attempt is made to employ the LRM to denoise the image which is subjected to guassian noise of certain variance. The performance analysis has been made in terms of calculated peak signal to noise ratio (PSNR).
References
J. F. Cai, E. J. Cand_es, and Z. Shen. A Singular Value Threshold-ing Algorithm for Matrix Completion. SIAM J. Opt., 20:1956, 2010.https://doi.org/10.1137/080738970.
E. J. Cand_es and Y. Plan. Matrix completion with noise.Proc. IEEE, 98(6):925{936, 2010.
W. Dai and O. Milenkovic. SET: an algorithm for consistent matrix completion. 2009. Availableat http://arxiv.org/abs/0909.2705.
Ankit Parekh and Ivan W. Selesnick, “Enhanced Low-Rank Matrix Approximation”, IEEE SIGNALPROCESSING LETTERS, VOL. 23, NO. 4, APRIL2016.
W. He, Y. Ding, Y. Zi, and I. W. Selesnick, “Sparsity-based algo-rithm for detecting faults in rotating machines,”Mech. Syst. Signal Process., vol. 72, pp. 46–64, 2016.https://doi.org/10.1016/j.ymssp.2015.11.027.
View more references (2)
H. Hu, J. Froment, and Q. Liu, “Patch-based low-rankminimization for image denoising,” [Online]. Availa-ble:http://arxiv.org/abs/1506.08353, 2015, pp. 1–4.
B. Huang, C. Mu, D. Goldfarb, and J. Wright, “Provablemodels for robust low-rank tensor completion,” Pac. J.Optim., vol. 11, no. 2, pp. 339–364, 2015.