Results 11 to 20 of about 143,672 (309)
Deconvolution with shapelets [PDF]
Astronomy & Astrophysics, 2008 We seek to find a shapelet-based scheme for deconvolving galaxy images from the PSF which leads to unbiased shear measurements. Based on the analytic formulation of convolution in shapelet space, we construct a procedure to recover the unconvolved shapelet coefficients under the assumption that the PSF is perfectly known. Using specific simulations, we
Melchior, Peter +3 more
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Blind Hierarchical Deconvolution [PDF]
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020 Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of model-based inversion techniques require knowledge on the convolution kernel to recover an accurate reconstruction and additionally prior assumptions on the regularity of the ...
Arttu Arjas +3 more
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, 2013 Deconvolution is a classic problem in many disciplines of engineering and science and frequently faced also in the study of physiological and pharmacokinetic systems. In this chapter, we first introduce the deconvolution problem for physiological systems
COBELLI, CLAUDIO +3 more
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IEEE Transactions on Information Theory, 2002 Summary: This paper studies the issue of optimal deconvolution density estimation using wavelets. The approach taken here can be considered as orthogonal series estimation in the more general context of the density estimation. We explore the asymptotic properties of estimators based on thresholding of estimated wavelet coefficients.
Jianqing Fan, Ja-Yong Koo
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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2014 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Se Un Parka +5 more
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SPIE Proceedings, 2012 In this paper we present a new approach to deblur the effect of atmospheric turbulence in the case of long range imaging. Our method is based on an analytical formulation, the Fried kernel, of the atmosphere modulation transfer function (MTF) and a framelet based deconvolution algorithm.
Jérôme Gilles, Stanley J. Osher
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Optics Express, 2012 The ability to retrieve information from different layers within a stratified sample using terahertz pulsed reflection imaging and spectroscopy has traditionally been resolution limited by the pulse width available. In this paper, a deconvolution algorithm is presented which circumvents this resolution limit, enabling deep sub-wavelength and sub-pulse ...
Gillian C. Walker +7 more
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Approximate Deconvolution Discretisation
Computers & Mathematics with Applications, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andrzej Boguslawski +2 more
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Blind deconvolution of sparse pulse sequences under a minimum distance constraint: a partially collapsed Gibbs sampler method [PDF]
, 2012 For blind deconvolution of an unknown sparse sequence convolved with an unknown pulse, a powerful Bayesian method employs the Gibbs sampler in combination with a Bernoulli–Gaussian prior modeling sparsity.
Kail, Georg +3 more
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aarjas/Hierarchical-deconvolution: Hierarchical deconvolution codes
, 2022 <p>This repository contains the deconvolution codes from the article</p> <p>Snizhana Ross, Arttu Arjas, Ilkka I Virtanen, Mikko J Sillanpää, Lassi Roininen, Andreas Hauptmann: Hierarchical Deconvolution for Incoherent Scatter Radar Data,
aarjas
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