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Learning Proximal Operators: Using Denoising Networks for Regularizing
  Inverse Imaging Problems

Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

11 April 2017
Tim Meinhardt
Michael Möller
C. Hazirbas
Daniel Cremers
ArXivPDFHTML

Papers citing "Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems"

3 / 53 papers shown
Title
Local Kernels that Approximate Bayesian Regularization and Proximal
  Operators
Local Kernels that Approximate Bayesian Regularization and Proximal Operators
Frank Ong
P. Milanfar
Pascal Getreuer
25
12
0
09 Mar 2018
Learning a Single Convolutional Super-Resolution Network for Multiple
  Degradations
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
K. Zhang
W. Zuo
Lei Zhang
SupR
44
902
0
17 Dec 2017
Learned Primal-dual Reconstruction
Learned Primal-dual Reconstruction
J. Adler
Ozan Oktem
MedIm
19
747
0
20 Jul 2017
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