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Blind Image Deconvolution By Neural Recursive Function Approximation
2010
Zenodo
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
doi:10.5281/zenodo.1333964
fatcat:5vzlnbyjlrawxpbzikzu4h2rtq