A novel approach to single frame multichannel blind image deconvolution is formulated recently as non-negative matrix factorization (NMF) problem with sparseness constraint imposed on the unknown mixing vector. Unlike most of the blind image deconvolution algorithms, the NMF approach requires no a priori knowledge about the blurring kernel and original image. The experimental performance evalua...

Deconvolution algorithms are commonly used to remove optical distortion from fluorescence microscopy images. Many such algorithms have been proposed, but those that produce the best image restoration results are iterative. Typically, each iteration involves one or more 3D convolutions, resulting in execution times of tens of seconds to several minutes for common image sizes on single-core compu...

We derive near optimal performance guarantees for subsampled blind deconvolution. Blind deconvolution is an ill-posed bilinear inverse problem and additional subsampling makes the problem even more challenging. Sparsity and spectral flatness priors on unknown signals are introduced to overcome these difficulties. While being crucial for deriving desired near optimal performance guarantees, unli...

In this paper, we propose a deconvolution method based on discrete-time optimal control. By combining Kalman filtering with optimal control, we state the problem in terms of tracking problem. This leads to solve a set of recurrent equations, including in particular a matrix Riccati equation. We present a method that transforms the solution of these recurrent equations in that of a linear system...

To reduce the influence of atmospheric turbulence on images of space-based objects we are developing a maximum a posteriori deconvolution approach. In contrast to techniques found in the literature, we are focusing on the statistics of the point-spread function (PSF) instead of the object. We incorporated statistical information about the PSF into multi-frame blind deconvolution. Theoretical co...

In this paper, we discuss blind deconvolution of dynamical systems, described by the state space model. First we formulate blind deconvolution problem in the framework of the state space model. The blind deconvolution is fulfilled in two stages: internal representation and signal separation. We employ two different learning strategies for training the parameters in the two stages. A sparse repr...

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A novel approach is presented in this paper to improve images which are altered by atmospheric turbulence. Two new algorithms are presented based on two combinations of a blind deconvolution block, an elastic registration block and a temporal filter block. The algorithms are tested on real images acquired in the desert in New Mexico by the NATO RTG40 group.

A new algorithm for maximum a posteriori deconvolution, Iterated Window Maximization (IWM), was recently proposed by Kaaresen. The algorithm was derived for a particular sparse spike train model, and was in a later simulation study found to perform better than a number of established alternatives. The purpose of the present paper is to show how the same simple and efficient technique can be gen...