Identification and restoration of noisy blurred images using the expectation-maximization algorithm

DOI10.1109/29.57545zbMath0713.93060OpenAlexW2124253112MaRDI QIDQ3199303

Jan Biemond, Reginald L. Lagendijk, D. E. Boekee

Publication date: 1990

Published in: IEEE Transactions on Acoustics, Speech, and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/0cc3baf59435fdd26e4328cae19e3b8fc18b3b0e

zbMATH Keywords

image restorationexpectation-maximization algorithmmaximum likelihood approachnoisy blurred imagesblur identification problem

Mathematics Subject Classification ID

Identification in stochastic control theory (93E12) Optimal stochastic control (93E20)

Related Items (10)

A restarted conjugate gradient method for ill-posed problems ⋮ A special Hermitian and skew-Hermitian splitting method for image restoration ⋮ Kronecker product approximations for image restoration with whole-sample symmetric boundary conditions ⋮ Combined blur, translation, scale and rotation invariant image recognition by Radon and pseudo-Fourier-Mellin transforms ⋮ Deconvolution of defocused image with multivariate local polynomial regression and iterative Wiener filtering in DWT domain ⋮ Blind superresolution ⋮ A robust algorithm for blind total variation restoration ⋮ Image restoration with a high-order total variation minimization method ⋮ Generalized Hermitian and skew-Hermitian splitting iterative method for image restoration ⋮ Convergence of the alternating minimization algorithm for blind deconvolution

This page was built for publication: Identification and restoration of noisy blurred images using the expectation-maximization algorithm