Variational blind deconvolution of multi-channel images

Variational blind deconvolution of multi-channel images

The fundamental problem of de-noising and de-blurring images is addressed in this study. The great difficulty in this task is due to the ill-posedness of the problem. We suggest to analyze multi-channel images to gain robustness and to regularize it by the Polyakov action which provides an anisotropic smoothing term that use intra-channel information. Blind de-convolution is then solved by addi...

Variational Deconvolution of Multi-channel Images with Inequality Constraints

A constrained variational deconvolution approach for multichannel images is presented. Constraints are enforced through a reparametrisation which allows a differential geometric reinterpretation. This view point is used to show that the deconvolution problem can be formulated as a standard gradient descent problem with an underlying metric that depends on the imposed constraints. Examples are g...

Variational Bayesian Blind Image Deconvolution: A review

In this paper we provide a review of the recent literature on Bayesian Blind Image Deconvolution (BID) methods. We believe that two events have marked the recent history of BID: the predominance of Variational Bayes (VB) inference as a tool to solve BID problems and the increasing interest of the computer vision community in solving BID problems. VB inference in combination with recent image mo...

Accelerated iterative blind deconvolution of still images

A variational method for Bayesian blind image deconvolution

In this paper the blind image deconvolution (BID) problem is solved using the Bayesian framework. In order to find the parameters of the proposed Bayesian model we present a new generalization of the expectation maximization (EM) algorithm based on the variational approximation methodology. The proposed variational-based algorithm for BID can be derived in closed form and can be implemented in ...