Bilevel Methods for Image Reconstruction
type: Item
instance of: scholarly article
title: Bilevel Methods for Image Reconstruction
DOI: 10.1561/2000000111
author: Caroline Crockett
author: Jeffrey A. Fessler
published in: Foundations and Trends® in Signal Processing
publication date: 2023-01-23T00:00:00Z
full work available at URL: https://arxiv.org/abs/2109.09610
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 94A08
zbMATH DE Number: 7644939
zbMATH Keywords: decoding and inverse problems
zbMATH Keywords: statistical/machine learning
zbMATH Keywords: sparse representations
describes a project that uses: LBFGS-B
describes a project that uses: DLMRI-Lab
describes a project that uses: U-Net
describes a project that uses: Julia
describes a project that uses: GitHub
MaRDI profile type: Publication
OpenAlex ID: W3199051821
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cites work: Julia: A Fresh Approach to Numerical Computing
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