Jiann-Ming Wu and Hsiao-Chang Chen and Chun-Chang Wu and Pei-Hsun Hsu
Blind Image Deconvolution by Neural Recursive Function Approximation
1383 - 1390
2010
4
10
International Journal of Mathematical and Computational Sciences
https://publications.waset.org/pdf/7527
https://publications.waset.org/vol/46
World Academy of Science, Engineering and Technology
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 shiftinvariant (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 nondeterministic 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.
Open Science Index 46, 2010