Results 211 to 220 of about 323,867 (242)

Deconvolution and Blind Deconvolution in Astronomy

2017
Fionn Murtagh Dept. Computer Science, Royal Holloway, University of London, Egham, UK e-mail: fmurtagh@acm.orgThis chapter reviews different astronomical deconvolution methods. The all-pervasive presence of noise is what makes deconvolution particularly difficult.
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Analysis of Bayesian Blind Deconvolution

2013
Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong priors on both the sharp image and blur kernel are required to regularize the solution space.
David P. Wipf, Haichao Zhang
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Blind Deconvolution With Model Discrepancies

IEEE Transactions on Image Processing, 2017
Blind deconvolution is a strongly ill-posed problem comprising of simultaneous blur and image estimation. Recent advances in prior modeling and/or inference methodology led to methods that started to perform reasonably well in real cases. However, as we show here, they tend to fail if the convolution model is violated even in a small part of the image.
Jan Kotera, Václav Smídl, Filip Sroubek   +2 more
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Gradient-projection blind deconvolution

Proceedings of 1st International Conference on Image Processing, 2002
We present a gradient-projection algorithm for solving the classical blind deconvolution problem. In our approach all known a priori information about both the unknown source and blurring functions is expressed via constraint sets. In computer simulations, the algorithm performed well even when the prior information was not accurate.
Yongyi Yang, Henry Stark, Nikolas P. Galatsanos   +2 more
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Relative optimization for blind deconvolution

IEEE Transactions on Signal Processing, 2005
We propose a relative optimization framework for quasi-maximum likelihood (QML) blind deconvolution and the relative Newton method as its particular instance. Special Hessian structure allows fast Newton system construction and solution, resulting in a fast-convergent algorithm with iteration complexity comparable to that of gradient methods.
Alexander M. Bronstein, Michael M. Bronstein, Michael Zibulevsky   +2 more
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Iteratively reweighted blind deconvolution

2013 IEEE International Conference on Image Processing, 2013
Traditional blind deconvolution techniques rely on a statistical model that relates the measured data to the pristine scene whose reconstruction is sought. If the data is not consistent with this forward model, then the reconstruction is badly degraded.
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Deconvolution without system model or a new blind deconvolution

ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
This paper is concerned with a new method of blind deconvolution. The deconvolution algorithms restore the input signal from the observed signal and "information" about the distorting system. This information may be expressed in terms of : \bullet transfer function \bullet frequency response \bullet state equation which are obtained from modelisation ...
Gérard Thomas, C. Dussaussois, F. Buret, Ph. Auriol   +3 more
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Blind deconvolution and phase retrieval

1988
Theoretical and practical aspects of identifying and deconvolving a convolution in more than one-dimension are presented. In contrast to conventional techniques which require knowledge of the blurring function, this thesis describes techniques for "blind" deconvolution.
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Blind deconvolution: a matter of norm

Computing in Science & Engineering, 2005
We continue the spectroscopy problem from the last issue, trying to reconstruct a true spectrum from an observed one. Again, we'll use blind deconvolution, but this time we'll impose some constraints on the error matrix E, leading to a more difficult problem to solve but often a more useful reconstruction.
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Robust blind spikes deconvolution

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
Blind spikes deconvolution, or blind super-resolution, deals with the problem of estimating the delays and amplitudes of spikes from its convolution with an unknown low-pass point spread function. By constraining the point spread function in a known low-dimensional subspace, a convex optimization algorithm called AtomicLift has been proposed to exactly
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