Geophysical measurements such as seismic datasets contain valuable information that originate from areas of interest in the subsurface; these reflections are however inevitably contaminated by other events created waves reverberating overburden. Multi-Dimensional Deconvolution (MDD) is a powerful technique used at various stages processing sequence to create ideal deprived overburden effects. W...

Kotlarski's identity has been widely used in applied economic research based on repeated‐measurement or panel models with latent variables. However, how to conduct inference for these an open question two decades. This paper addresses this problem by constructing a novel confidence band the density function of variable repeated measurement error model. The builds our finding that we can rewrite...

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In this paper, an iterative method for robust deconvolution with positivity constraints is discussed. It is based on the known variational interpretation of the Richardson-Lucy iterative deconvolution as fixed-point iteration for the minimisation of an information divergence functional under a multiplicative perturbation model. The asymmetric penaliser function involved in this functional is th...

Several methods for solving efficiently the one-dimensional deconvolution problem are proposed. The problem is to solve the Volterra equation ku := ∫ t 0 k(t − s)u(s)ds = g(t), 0 ≤ t ≤ T . The data, g(t), are noisy. Of special practical interest is the case when the data are noisy and known at a discrete set of times. A general approach to the deconvolution problem is proposed: represent k = A(...

INTRODUCTION: A promising technique for Cardiac perfusion analysis and ischemia detection is rapid MRI following a bolus injection of a contrast agent. This is followed by quantitative kinetic analysis of the contrast agent during first pass [1]. The perfusion analysis method proposed in this study is an implementation of the central volume principle to calculate myocardial blood flow using dec...

The desire to recover the unknown density when data are contaminated with errors leads to nonparametric deconvolution problems. The difficulty of deconvolution depends on both the smoothness of error distribution and the smoothness of the priori. Under a general class of smoothness constraints, we show that deconvolution kernel density k-l estimates achieve the best attainable global rates of c...

It has been recently shown that compressive sampling is an interesting perspective for fast ultrasound imaging. This paper addresses the problem of compressive deconvolution for ultrasound imaging systems using an assumption of generalized Gaussian distributed tissue reflectivity function. The benefit of compressive deconvolution is the joint volume reduction of the acquired data and the image ...