This vignette motivates and describes the functionalities of the CellMix package, an R package for performing gene expression deconvolution analysis. The package defines a general framework to apply, develop and test gene expression deconvolution methods. It incorporates, generalises and extends the set of tools we implemented when developing a semi-supervised approach to this problem, and incl...

We present a novel solution technique for the blind subspace deconvolution (BSSD) problem, where temporal convolution of multidimensional hidden independent components is observed and the task is to uncover the hidden components using the observation only. We carry out this task for the undercomplete case (uBSSD): we reduce the original uBSSD task via linear prediction to independent subspace a...

A new method of the multichannel blind deconvolution is introduced. The algorithm is simple, computationally efficient and does not need any statistical presumptions about the image. Any a priori knowledge about the PSF and the original can be incorporated very easily. The numerical simulations and experiments give promising results comparing to other restoration methods.

This paper introduces a novel blind on-line dereverberation algorithm for speech signals based on a natural gradient deconvolution algorithm. Existing blind dereverberation algorithms often employ a structure that performs speech prewhitening using LPC analysis followed by a blind deconvolution step applied to the LPC residual. Here it is shown that better performance can be achieved by respect...

Various applications in signal processing and machine learning give rise to highly structured spectral optimization problems characterized by low-rank solutions. Two important examples that motivate this work are optimization problems from phase retrieval and from blind deconvolution, which are designed to yield rank-1 solutions. An algorithm is described based on solving a certain constrained ...

Single image blind deconvolution aims to estimate the unknown blur from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and typical approaches involve some heuristic or other steps without clear mathematical explanation to arrive at an acceptable solution. We show that a straightforward maximum a posteriori estimation incorporating sparse pr...

Nowadays, deconvolution in cell and tissue imaging has matured into a standard restoration technique that is accessible to large fraction of the microscopy community thanks to steadily improving algorithms. Still, deconvolution is often the rate-limiting step in the analysis of the acquired data, even at today's computer performance. Here, we present the Huygens Remote Manager, an open-source, ...

Abstract Blind deconvolution techniques applied to spatially and/or temporally oversampled signals have recently attracted much interest in the research community. This contribution contains an analysis of experimental data collected from an antenna array in a suburban environment. The Noise Subspace (NS) technique of [1] and the Linear Prediction (LP) method of [2, 4] are examined. The real da...

[1] The waves generated by faulting represent the primary threat posed by most large earthquakes. The effect of complex geological structures, such as sedimentary basins, on earthquake ground motion is a source of particular concern. We show that it is possible to extract reliable phase and amplitude response that includes the effects of complex structure for the elastodynamic Green’s function ...

[1] The methodology of surface‐wave retrieval from ambient seismic noise by crosscorrelation relies on the assumption that the noise field is equipartitioned. Deviations from equipartitioning degrade the accuracy of the retrieved surface‐wave Green’s function. A point‐spread function, derived from the same ambient noise field, quantifies the smearing in space and time of the virtual source of t...