Via a simulation study we compare the finite sample performance of the deconvolution kernel density estimator in the supersmooth deconvolution problem to its asymptotic behaviour predicted by two asymptotic normality theorems. Our results indicate that for lower noise levels and moderate sample sizes the match between the asymptotic theory and the finite sample performance of the estimator is n...

We use blind deconvolution methods in optical diffusion tomography to reconstruct images of objects imbedded in or located behind turbid media from continuous-wave measurements of the scattered light transmitted through the media. In particular, we use a blind deconvolution imaging algorithm to determine both a deblurred image of the object and the depth of the object inside the turbid medium. ...

There has recently been considerable interest in applying Total Variation regularization with an ` data fidelity term to the denoising of images subject to salt and pepper noise, but the extension of this formulation to more general problems, such as deconvolution, has received little attention. We consider this problem, comparing the performance of `TV deconvolution, computed via our Iterative...

This paper introduces a framework based on the LMS algorithm for sequential deconvolution of images acquired by a pushbroom hyperspectral imaging system. Considering a sequential model of image blurring phenomenon, we derive a zero-attracting LMS (ZA-LMS) algorithm for 2D image deconvolution. Its transient behavior is analyzed in the mean and mean-square sense. For hyperspectral images, a spect...

A defocus blur metric for use in blind image quality assessment is proposed. Blind image deconvolution methods are used to determine the metric. Existing direct deconvolution methods based on the cepstrum, bicepstrum and on a spectral subtraction technique are compared across 210 images. A variation of the spectral subtraction method, based on a power spectrum surface of revolution, is proposed...

This study attempts to develop a time-scale deconvolution filter for optimal signal reconstruction of nonstationary processes with a stationary increment transmitted through a multipath fading and colored noisy channel with stochastic tap coefficients. A deconvolution filter based on wavelet analysis/synthesis filter bank is proposed to solve this problem via a three-stage filter bank. A fracta...

We introduce an approximate message passing (AMP) algorithm for the problem of image deconvolution. The recovery problem is formulated in Bayesian terms, and uses sparse statistical priors for estimating the minimum-mean-squared-error solution. Our setting differs from previous investigations where AMP was considered for sparse signal recovery from random or Fourier measurements. AMP is incompa...

We propose a new two-stage blind separation and deconvolution (BSD) algorithm for a convolutive mixture of speech, in which a new Single-Input Multiple-Output (SIMO)-model-based ICA (SIMOICA) and blind multichannel inverse filtering are combined. SIMOICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the...

This article reviews different deconvolution methods. The all-pervasive presence of noise is what makes deconvolution particularly difficult. The diversity of resulting algorithms reflects different ways of estimating the true signal under various idealizations of its properties. Different ways of approaching signal recovery are based on different instrumental noise models, whether the astronom...

In this paper, we study convergence and e ciency of the batch estimator and natural gradient algorithm for blind deconvolution. First, the blind deconvolution problem is formulated in the framework of a semiparametric model, and a family of estimating functions is derived for blind deconvolution. To improve the learning e ciency of the online algorithm, explicit standardized estimating function...