Blind deconvolution is an ubiquitous non-linear inverse problem in applications like wireless communications and image processing. This problem is generally ill-posed, and there have been efforts to use sparse models for regularizing blind deconvolution to promote signal identifiability. Part I of this two-part paper characterizes the ambiguity space of blind deconvolution and shows unidentifia...

We apply a total variation minimization technique for the deblurring of two-dimensional images. This yields an integro-diierential equation with a nonlinear elliptic partial diierential operator. In this paper we discuss a numerical scheme for the eecient solution of this integro-diierential equation. Results are presented for a deconvolution problem arising in ground-based astronomical imaging.

This paper addresses an open question of how to devise numerical schemes for approximate deconvolution fluid flow models that are efficient, unconditionally stable, and optimally accurate. We propose, analyze and test a scheme for these models that has each of these properties for the case of homogeneous Dirichlet velocity boundary conditions. There are several important components to the deriv...

Convolution and Deconvolution is having wide area of application in Digital Signal Processing. Convolution helps to estimate the output of a system with arbitrary input, with knowledge of impulse response of the system. Linear systems characteristics are completely specified by the systems impulse response, as governed by the mathematics of convolution. And with the knowledge of impulse respons...

Image deconvolution is a challenging ill-posed problem when only partial information of the blur kernel is available. Certain regularization on sharp images has to be imposed to constrain the estimation of true images during the blind deconvolution process. Based on the observation that an image of sharp edges tends to minimize the ratio between the `1 norm and the `2 norm of its wavelet frame ...

This paper addresses the blind deconvolution of multi-input– multi-output (MIMO) FIR systems driven by white non-Gaussian source signals. First, we present a weaker condition on source signals than the so-called i.i.d. condition so that blind deconvolution is possible. Then, under this condition, we provide a necessary and sufficient condition for blind deconvolution of MIMO FIR systems. Finall...

Multichannel blind deconvolution has received increasing attention during the last decade. Recently, Martone [3, 4] extended the super-exponential method proposed by Shalvi and Weinstein [1, 2] for single-channel blind deconvolution to multichannel blind deconvolution. However, the Martone extension suffers from two type of serious drawbacks. The objective of this paper is to obviate these draw...

We carry out a classification of the observed pulsar dataset into distinct clusters, based on $P-\dot{P}$ diagram, using Extreme Deconvolution Gaussian Mixture Model. then use Bayesian Information Criterion to select optimum number clusters. find in accord with previous works, that can be optimally classified six two for millisecond population, and four ordinary population. Beyond that, however...

Blind deconvolution is an ubiquitous non-linear inverse problem in applications like wireless communications and image processing. This problem is generally ill-posed since signal identifiability is a key concern, and there have been efforts to use sparse models for regularizing blind deconvolution to promote signal identifiability. Part I of this two-part paper establishes a measure theoretica...