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Deconvolution

Known as: Deconvolved, Focus restoration 
In mathematics, deconvolution is an algorithm-based process used to reverse the effects of convolution on recorded data. The concept of deconvolution… Expand
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AIPSAlgorithmArtificial neuronBioimage informatics
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Broader (1)

Signal processing

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Applications , Promises , and Pitfalls of Deep Learning for Fluorescence Image Reconstruction
Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence… Expand
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Highly Cited
2016
Highly Cited
2016
Deconvolution and Checkerboard Artifacts
 
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Highly Cited
2015
Highly Cited
2015
Learning Deconvolution Network for Semantic Segmentation
We propose a novel semantic segmentation algorithm by learning a deep deconvolution network. We learn the network on top of the… Expand
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Highly Cited
2015
Highly Cited
2015
Learning Deconvolution Network for Semantic Segmentation
 
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Highly Cited
2014
Highly Cited
2014
Deep Convolutional Neural Network for Image Deconvolution
Many fundamental image-related problems involve deconvolution operators. Real blur degradation seldom complies with an ideal… Expand
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Highly Cited
2010
Highly Cited
2010
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
Bregman methods introduced in [S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, Multiscale Model. Simul., 4 (2005), pp. 460… Expand
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Highly Cited
2009
Highly Cited
2009
Fast Image Deconvolution using Hyper-Laplacian Priors
The heavy-tailed distribution of gradients in natural scenes have proven effective priors for a range of problems such as… Expand
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Highly Cited
2004
Highly Cited
2004
Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sources from Monophonic Inputs
In this paper we present an extension to the Non-Negative Matrix Factorization algorithm which is capable of identifying… Expand
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Highly Cited
1999
Highly Cited
1999
Iterative deconvolution and receiver-function estimation
We describe and apply an iterative, time-domain deconvolution approach to receiver-function estimation and illustrate the… Expand
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Highly Cited
1991
Highly Cited
1991
On the Optimal Rates of Convergence for Nonparametric Deconvolution Problems
 
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