We propose a probabilistic model for the Independent Vector Analysis approach to blind deconvolution and derive an asymptotic Newton method to estimate the model by Maximum Likelihood.

Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biological samples. Unfortunately, dealing with deconvolution tools is not straightforward. Among others, end users have to select the appropriate a...

The kinetic analysis of complex solid-state reactions that involve simultaneous overlapping processes is challenging. A method that involves the deconvolution of the individual processes from the overall differential kinetic curves obtained under linear heating rate conditions, followed by the kinetic analysis of the discrete processes using combined kinetic analysis, is proposed. Different con...

Blind deconvolution problems arise in image analysis when both the extent of image blur, and the true image, are unknown. If a model is available for at least one of these quantities then, in theory, the problem is solvable. It is generally not solvable if neither the image nor the point-spread function, which controls the extent of blur, is known parametrically. In this paper we develop method...

Program dDeconvolve was developed to provide deconvolution analysis of FMRI time series data This has two primary applications estimation of the system impulse response function and multiple linear regression analysis of time series data Given the input stimulus function s and the measured FMRI signal data program dDeconvolve rst estimates the impulse response function s the impulse response fu...

A different procedure for calculating linear and nonlinear coefficients of model systems for fully developed turbulence is derived. This procedure can be applied to systems with multiple interacting fields; in the single-field case the linear coefficients consist of mode frequencies and growth rates. This method differs from previous methods in the use of a limited set of functions or basis set...

In performing blind deconvolution to remove reverberation from speech signal, most acoustic deconvolution filters need a great many number of taps, and acoustic environments are often time-varying. Therefore, deconvolution filter coefficients should find their desired values with limited data, but conventional methods need lots of data to converge the coefficients. In this paper, we use sparse ...

A new technique, root projection (RP), is given for quantitative deconvolution of causal time series in the presence of moderate amounts of noise. Deconvolution is treated as a well-conditioned but underdetermined problem and a priori information is employed to obtain comparable noise reduction to that achieved by singular value decomposition (SVD) techniques while providing more accurate frequ...

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. Standard quantification techniques attempt to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition. These techniques fail to account for adjacent signals which can l...