doi: 10.7763/IJET.2011.V3.285
Analysis of Blind Separation of Noisy Mixed Images Based on Wavelet Thresholding and Independent Component Analysis
Alka Mahajan
,
Gajanan Birajdar
- S.I.E.S. Graduate School of Technology, Navi Mumbai, India.
Abstract
This paper investigates the technique of wavelet threshold de-noising with Independent Component Analysis (ICA) for noisy image separation. In the first approach, noisy mixed images are separated using fast ICA algorithm and then wavelet thresholding is used to de-noise. The second approach uses wavelet threshold to de-noise and then use the fast ICA algorithm to separate the de-noised images. The simulation results show better performance of image separation followed by denoising rather than the other way round. Peak Signal to Noise Ratio (PSNR), Improved Signal to Noise Ratio (ISNR), Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) are used to evaluate quality of separated images. Amari error and structural similarity index (SSIM) is computed for the separation quality measurement.
Keywords
- Blind source separation (BSS)
- fast ICA
- Independent component analysis (ICA)
- Wavelet threshold
How to Cite
Alka Mahajan and Gajanan Birajdar, "Analysis of Blind Separation of Noisy Mixed Images Based on Wavelet Thresholding and Independent Component Analysis," International Journal of Engineering and Technology, vol. 3, no. 5, pp. 560-564, 2011. https://doi.org/ 10.7763/IJET.2011.V3.285
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).