A Fast Iterative Blind Image Restoration Algorithm Successful blind image deconvolution algorithms require the exact estimation of the Point Spread Function size, PSF. In the absence of any priori information about the imagery system and the true image, this estimation is normally done by trial and error experimentation, until an acceptable restored image quality is obtained. This paper, presents an exact estimation of the PSF size that yields the optimum restored image quality. The paper also describes a least squares PSF estimation, instead of the slow iterative update, that is commonly used in Iterative Blind Deconvolution software, IBD. Moreover, a technique is also proposed to improve the sharpness of the deconvolved images, by constrained maximization of the detail wavelet-coefficient entropies. Several simulations are given to verify these results.

This paper presents a new method for solving post-nonlinear blind source separation (PNLBSS). It proposes a modified Gaussianization technique for recovering PNLBSS systems. The proposed technique overcomes the failure of classical Gaussianization schemes to work properly in some PNL mixture with severe nonlinearity characteristics. It is found that the failure is due to the multi-modality of the probability distributions (pdf), of the received nonlinear mixture. In order to estimate the eceived pdf, the paper proposes an accurate nonparametric evaluation of the pdf signal and its entropy functions . The pdf estimation is based on using Bspline wavelet transform as the smoothing filter for the data histogram distribution. The paper also proposes a pre-mapping scheme that transforms multi-modal pdf to a uni-modal one, and thereby makes them Gaussianable. Several illustrative examples are given, to verify the ability of the proposed technique to estimate pdf signal, recover PNLBSS mixture with severe nonlinearity characteristics.

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