Blind separation of mixed sources with curvelet de-noising

Author

Abbass, M.Y. ; Shehata, S.A. ; Haggag, S.S. ; Diab, S.M. ; Sallam, B.M. ; El-Rabaie, Sayed ; Abd El-Samie, F.E.

Author_Institution

Dept. of Eng., Atomic Energy Authority, Cairo, Egypt

fYear

2013

fDate

Aug. 31 2013-Sept. 2 2013

Firstpage

227

Lastpage

231

Abstract

This paper investigates the technique of Fast Discrete Curvelet Transform (FDCT) de-noising with the Independent Component Analysis (ICA) for the separation of signals from noisy mixtures. Two approaches are presented for this purpose. In the first approach, the signals are separated using the fast ICA algorithm, and then curvelet thresholding is used to de-noise the results. The second approach uses curvelet thresholding to denoise the mixtures, and then the fast ICA algorithm to separate the signals from these mixtures. The simulation results show a better performance for image de-noising followed by separation. The Signal-to-Noise Ratio (SNR), and Peak Signal-to-Noise Ratio (PSNR) are used as quality evaluation metrics with signals and images, respectively.

Keywords

blind source separation; curvelet transforms; discrete transforms; image denoising; independent component analysis; FDCT de-noising; PSNR; curvelet thresholding; fast ICA algorithm; fast discrete curvelet transform denoising; image de-noising; independent component analysis; mixed source blind separation; peak signal-to-noise ratio; quality evaluation metrics; signal separation; Algorithm design and analysis; Independent component analysis; Noise reduction; Signal processing algorithms; Signal to noise ratio; Transforms; Blind Source Separation (BSS); FDCT; ICA;

fLanguage

English

Publisher

ieee

Conference_Titel

Modelling, Identification & Control (ICMIC), 2013 Proceedings of International Conference on

Conference_Location

Cairo

Print_ISBN

978-0-9567157-3-9

Type

conf

Filename

6642183