Results 161 to 170 of about 25,670 (189)

Normalized Blind Deconvolution

2018
We introduce a family of novel approaches to single-image blind deconvolution, i.e., the problem of recovering a sharp image and a blur kernel from a single blurry input. This problem is highly ill-posed, because infinite (image, blur) pairs produce the same blurry image.
Meiguang Jin, Stefan Roth, Paolo Favaro
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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, Vaclav Smidl, Filip Sroubek
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Blind image deconvolution revisited

IEEE Signal Processing Magazine, 1996
The article discusses the major approaches, such as projection based blind deconvolution and maximum likelihood restoration, we overlooked previously (see ibid., no.5, 1996). We discuss them for completeness along with some other works found in the literature.
D. Kundur, D. Hatzinakos
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Near optimal blind deconvolution

ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003
A solution is proposed for blind deconvolution problems, i.e. the estimation of the impulse response of an unknown discrete-time channel given the output data sequence and statistical information on the input sequence. The solution approaches the optimal one when the input sequence is independent and identically distributed and the channel distortion ...
S. Bellini, F. Rocca
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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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MRF-Based Blind Image Deconvolution

2013
This paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our
Komodakis, Nikos, Paragios, Nikos
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Sparse multichannel blind deconvolution

GEOPHYSICS, 2014
We developed a sparse multichannel blind deconvolution (SMBD) method. The method is a modification of the multichannel blind deconvolution technique often called Euclid deconvolution, in which the multichannel impulse response of the earth is estimated by solving an homogeneous system of equations.
Nasser Kazemi, Mauricio D. Sacchi
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Multichannel blind image deconvolution

Proceedings of 2012 9th International Bhurban Conference on Applied Sciences & Technology (IBCAST), 2012
A new method has been proposed for performing multichannel blind image deconvolution. We consider the image deconvolution problem when limited information about the Point Spread Function (PSF) is available and the original image is of sparse nature. It is assumed that the original image is corrupted by a degradation function (i.e.
Umer Javed, Muhammad Mohsin Riaz, null Tanveer Ahmad Cheema   +2 more
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Blind Image Deconvolution

2017
BLIND IMAGE DECONVOLUTION: PROBLEM FORMULATION AND EXISTING APPROACHES Tom E. Bishop, S. Derin Babacan, Bruno Amizic, Aggelos K. Katsaggelos, Tony Chan, and Rafael Molina Introduction Mathematical Problem Formulation Classification of Blind Image Deconvolution Methodologies Bayesian Framework for Blind Image Deconvolution Bayesian Modeling of Blind ...
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Blind deconvolution using temporal predictability

Neurocomputing, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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