Maximum a posteriori blind image deconvolution with Huber-Markov random-field regularization.
We propose a maximum a posteriori blind deconvolution approach using a Huber-Markov random-field model. Compared with the conventional maximum-likelihood method, our algorithm not only suppresses noise effectively but also significantly alleviates the artifacts produced by the deconvolution process. The performance of this method is demonstrated by computer simulations.
Imaging Systems Laboratory, Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong.
Zhimin
Z
Xu
Edmund Y
EY
Lam