الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Image denoising is one of the most significant tasks in image processing,
analysis and image processing applications. Source signal reconstruction is one
of the forms of image denoising, in which we need to retrieve the source signal
or image from a noised version, from a mixture with other signals or from a
version with missing parts. ICA is a statistical signal processing technique that
was originally proposed to find the latent source signals from observed mixture
signal without knowing any prior knowledge of the mixing mechanism.
The first contribution of our work is proposing a new algorithm SCG-ICA for
blind signal separation. The new algorithm significantly improves the
convergence rate of gradient-based blind source separation. The proposed
algorithm is based on the scaled conjugate gradient method, which used to
optimize the kurtosis contrast function in order to estimate the demixing
matrix.
The second contribution is proposing an ICA based dictionary learning
method, which uses the observed data in the learning instead of using fixed
dictionary. In order to use the ICA for learning the dictionary, we used the
FastICA algorithm under the assumption that the source signals have a
Generalized Gamma Density (GGD), which
shows a good result in learning the dictionary basis. The results show that
learning
the dictionary using the ICA are suitable for image denoising and inpainting.
We also propose using the Generalized K Distribution (GKD) as an alternative
to the Generalized Gamma Density (GGD), and it is also give good results in
image denoising.