The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined, reference gene expression profiles of the constituent populations in these samples. However, the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to g...

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.

In this paper the multiframe blind deconvolution method 1] for the identiication and correction of turbulence-induced phase aberrations in a ground-based telescope is extended to include the gains, backgrounds, and biases inherent in many charge-coupled device (CCD) cameras. In addition, an application of this method to real telescope data is described.

Generalized Linnik processes and associated logarithmic diffusion equations can be constructed by appropriate Bochner randomization of the time variable in Brownian motion and the related heat conduction equation. Remarkably, over a large but finite frequency range, generalized Linnik characteristic functions can exhibit almost Gaussian behavior near the origin, while behaving like low exponent...

We perform an analysis of the TRACE telescope blur from EUV images. The blur pattern is discussed in terms of the telescope point spread function (PSF) for the 171 Å filter. The analysis performed consists of two steps. First an initial shape for the PSF core is determined directly from TRACE EUV images. Second, the blind deconvolution approach is used for obtaining the final PSF shape. The PSF...

We consider a deconvolution model for 3D periodic flows. We show the existence of a global attractor for the model. MCS Classification : 76D05, 35Q30, 76F65, 76D03 Key-words : Navier-Stokes equations, Large eddy simulation, Deconvolution models.

We extend deconvolution in a periodic setting to deal with functional data. The resulting functional deconvolution model can be viewed as a generalization of a multitude of inverse problems in mathematical physics where one needs to recover initial or boundary conditions on the basis of observations from a noisy solution of a partial differential equation. In the case when it is observed at a f...

We extend deconvolution in a periodic setting to deal with functional data. The resulting functional deconvolution model can be viewed as a generalization of a multitude of inverse problems in mathematical physics where one needs to recover initial or boundary conditions on the basis of observations from a noisy solution of a partial differential equation. In the case when it is observed at a f...

In this paper, we theoretically discuss about regularization on logarithm bidirectional deconvolution proposed by Claerbout et al. (2011). We hope model fitting helps reduce the unwelcome strong precursors and noise in the bidirectional deconvolution results. INTRODUCTION Bidirectional deconvolution is an ill-posed and highly non-linear problem with many unexpected traps-local minima. When we t...