Rotating machinery vibration analysis involves a convolute mixture because of the propagation medium, and the signals recorded by sensors in an industrial application are often disrupted by the environment. Deconvolution is a signal processing method for convolution of vibration sources, spectral kurtosis is a statistical tool which can indicate the presence of series of transients and their lo...

Blind deconvolution is considered as a problem of quasi maximum likelihood (QML) estimation of the restoration kernel. Simple closed-form expressions for the asymptotic estimation error are derived. The asymptotic performance bounds coincide with the Cramér-Rao bounds, when the true ML estimator is used. Conditions for asymptotic stability of the QML estimator are derived. Special cases when th...

We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additio...

We address for the first time the issue of motion blur in light field images captured from plenoptic cameras (instead of camera arrays), where the spatial sampling in each view is decimated. We propose a solution to the estimation of a sharp light field given a blurry one, when the motion blur point spread function is unknown, i.e., the so-called blind deconvolution problem. Unfortunately, the ...

In this paper, we address a sequence estimation problem formulated as a kind of blind deconvolution problem. We employ Bayesian estimation approach and treat this problem. As a solution to this problem, we present a sequence estimation method using EM (Expectation – Maximization) algorithm. The proposed method is applied to the watermark detection problem, which also becomes a blind deconvoluti...

Blind deconvolution and demixing is the problem of reconstructing convolved signals kernels from sum their convolutions. This arises in many applications, such as blind MIMO. work presents a separable approach to via convex optimization. Unlike previous works, our formulation allows separation into smaller optimization problems, which significantly improves complexity. We develop recovery guara...

Time-domain bidirectional deconvolution methods show great promise for overcoming the minimum-phase assumption in blind deconvolution of signals containing a mixed-phase wavelet, such as seismic data. However, usually one timedomain method is slow to converge (the slalom method) and the other one is sensitive to the initial point or preconditioner (the symmetric method). Claerbout proposed a lo...