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.
—Blind source separation (BSS), fast ICA, Independent component analysis (ICA), Wavelet threshold.
Alka Mahajan and Gajanan Birajdar are with S.I.E.S. Graduate School of Technology, Navi Mumbai, India. (e-mail: email@example.com; firstname.lastname@example.org).
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.