%0 Journal Article
	%A Jiann-Ming Wu and  Hsiao-Chang Chen and  Chun-Chang Wu and  Pei-Hsun Hsu
	%D 2010
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 46, 2010
	%T Blind Image Deconvolution by Neural Recursive Function Approximation
	%U https://publications.waset.org/pdf/7527
	%V 46
	%X This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.

	%P 1383 - 1390