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Modeling Realistic Degradations in Non-blind Deconvolution

Modeling Realistic Degradations in Non-blind Deconvolution

4 June 2018
J. Anger
M. Delbracio
Gabriele Facciolo
ArXiv (abs)PDFHTML

Papers citing "Modeling Realistic Degradations in Non-blind Deconvolution"

5 / 5 papers shown
Title
Double Blind Imaging with Generative Modeling
Double Blind Imaging with Generative Modeling
Brett Levac
A. Jalal
Kannan Ramchandran
Jonathan I. Tamir
DiffMMedIm
145
0
0
27 Mar 2025
Iterative Reweighted Least Squares Networks With Convergence Guarantees
  for Solving Inverse Imaging Problems
Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems
Iaroslav Koshelev
Stamatios Lefkimmiatis
71
1
0
10 Aug 2023
INFWIDE: Image and Feature Space Wiener Deconvolution Network for
  Non-blind Image Deblurring in Low-Light Conditions
INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions
Zhihong Zhang
Yuxiao Cheng
J. Suo
Liheng Bian
Qionghai Dai
100
19
0
17 Jul 2022
End-to-end Learning for Joint Depth and Image Reconstruction from
  Diffracted Rotation
End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation
Mazen Mel
M. Siddiqui
Pietro Zanuttigh
MDE
67
7
0
14 Apr 2022
Polyblur: Removing mild blur by polynomial reblurring
Polyblur: Removing mild blur by polynomial reblurring
M. Delbracio
Ignacio Garcia Dorado
Sungjoon Choi
D. Kelly
P. Milanfar
71
28
0
16 Dec 2020
1