Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/springer-journals/image-recovery-via-nonlocal-operators-egvehyO90R
References (32)
-
N. Sochen (2004)
Affine Invariant Flows in the Beltrami FrameworkJournal of Mathematical Imaging and Vision, 20
-
D. Donoho, I. Johnstone (1994)
Ideal spatial adaptation by wavelet shrinkageBiometrika, 81
-
T. Goldstein, S. Osher (2009)
The Split Bregman Method for L1-Regularized ProblemsSIAM J. Imaging Sci., 2
-
A. Marquina, S. Osher (2008)
Image Super-Resolution by TV-Regularization and Bregman IterationJournal of Scientific Computing, 37
-
Guy Gilboa, S. Osher (2008)
Nonlocal Operators with Applications to Image ProcessingMultiscale Model. Simul., 7
-
(1983)
Gaussian blur and the heat equation: forward and inverse solutions -
A. Chambolle (2004)
An Algorithm for Total Variation Minimization and ApplicationsJournal of Mathematical Imaging and Vision, 20
-
L. I︠A︡roslavskiĭ (1985)
Digital picture processing : an introduction -
G. Peyré, S. Bougleux, L. Cohen (2008)
Non-local Regularization of Inverse Problems -
Lin He, A. Marquina, S. Osher (2005)
Blind deconvolution using TV regularization and Bregman iterationInternational Journal of Imaging Systems and Technology, 15
-
A. Chambolle (2004)
Practical, Unified, Motion and Missing Data Treatment in Degraded VideoJournal of Mathematical Imaging and Vision, 20
-
Alexei Efros, T. Leung (1999)
Texture synthesis by non-parametric samplingProceedings of the Seventh IEEE International Conference on Computer Vision, 2
-
M. Glas (2000)
Principles of Computerized Tomographic Imaging -
D. Goldfarb, W. Yin (2005)
Second-order Cone Programming Methods for Total Variation-Based Image RestorationSIAM J. Sci. Comput., 27
-
G. Peyré, S. Bougleux, L. Cohen (2008)
ECCV ’08: European Conference on Computer Vision -
Jianhong Shen, T. Chan (2002)
Mathematical Models for Local Nontexture InpaintingsSIAM J. Appl. Math., 62
-
Guy Gilboa, S. Osher (2007)
Nonlocal Linear Image Regularization and Supervised SegmentationMultiscale Model. Simul., 6
-
R. Hummel, B. Kimia, S. Zucker (2015)
Deblurring Gaussian blurComput. Vis. Graph. Image Process., 38
-
S. Kindermann, S. Osher, Peter Jones (2005)
Deblurring and Denoising of Images by Nonlocal FunctionalsMultiscale Model. Simul., 4
-
(2007)
Fast discrete optimization for sparse approximations and deconvolution -
Jia Li, A. Hero (2004)
A Fast Spectral Method for Active 3D Shape ReconstructionJournal of Mathematical Imaging and Vision, 20
-
H. Kramer, J. Bruckner (1975)
Iterations of a non-linear transformation for enhancement of digital imagesPattern Recognit., 7
-
H.C. Andrews, B.R. Hunt (1977)
Digital Image Restoration -
L. Rudin, S. Osher, E. Fatemi (1992)
Nonlinear total variation based noise removal algorithmsPhysica D: Nonlinear Phenomena, 60
-
A. Marquina (2009)
Nonlinear Inverse Scale Space Methods for Total Variation Blind DeconvolutionSIAM J. Imaging Sci., 2
-
(1977)
Solution of Ill-Posed Problems -
T. Chan, Chiu-Kwong Wong (1998)
Total variation blind deconvolutionIEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 7 3
-
A. Buades, B. Coll, J. Morel (2005)
A Review of Image Denoising Algorithms, with a New OneMultiscale Model. Simul., 4
-
Dengyong Zhou, B. Scholkopf (2004)
A Regularization Framework for Learning from Graph Data -
L. Yaroslavsky (1985)
Digital Picture Processing -
D. Donoho (1994)
Ideal spacial adaptation via wavelet shrinkage, 81
-
(2006)
Image enhancement by non-local reverse heat equation
- Publisher
- Springer Journals
- Copyright
- Copyright © 2009 by The Author(s)
- Subject
- Mathematics; Theoretical, Mathematical and Computational Physics; Appl.Mathematics/Computational Methods of Engineering; Computational Mathematics and Numerical Analysis; Algorithms
- ISSN
- 0885-7474
- eISSN
- 1573-7691
- DOI
- 10.1007/s10915-009-9320-2
- Publisher site
- See Article on Publisher Site
Abstract
This paper considers two nonlocal regularizations for image recovery, which exploit the spatial interactions in images. We get superior results using preprocessed data as input for the weighted functionals. Applications discussed include image deconvolution and tomographic reconstruction. The numerical results show our method outperforms some previous ones.
Journal
Journal of Scientific Computing – Springer Journals
Published: Aug 27, 2009