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Perturbation of the Eigenvectors of the Graph Laplacian: Application to
  Image Denoising

Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising

29 February 2012
François G. Meyer
X. Shen
ArXiv (abs)PDFHTML

Papers citing "Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising"

15 / 15 papers shown
Title
Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space
Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space
Kelum Gajamannage
D. Jayathilake
Maria Vasilyeva
50
0
0
15 Apr 2025
Efficient Image Denoising by Low-Rank Singular Vector Approximations of
  Geodesics' Gramian Matrix
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix
Kelum Gajamannage
Yonggi Park
S. Mallikarjunaiah
Sunil Mathur
21
1
0
27 Sep 2022
Robust Inference of Manifold Density and Geometry by Doubly Stochastic
  Scaling
Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling
Boris Landa
Xiuyuan Cheng
101
6
0
16 Sep 2022
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise
  Sampled From Diverse Probability Distributions
Geodesic Gramian Denoising Applied to the Images Contaminated With Noise Sampled From Diverse Probability Distributions
Yonggi Park
Kelum Gajamannage
Alexey L. Sadovski
DiffM
21
6
0
04 Mar 2022
Impact of signal-to-noise ratio and bandwidth on graph Laplacian
  spectrum from high-dimensional noisy point cloud
Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud
Xiucai Ding
Hau‐Tieng Wu
121
13
0
21 Nov 2020
Image Denoising Using the Geodesics' Gramian of the Manifold Underlying
  Patch-Space
Image Denoising Using the Geodesics' Gramian of the Manifold Underlying Patch-Space
Kelum Gajamannage
46
4
0
14 Oct 2020
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to
  Heteroskedastic Noise
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise
Boris Landa
Ronald R. Coifman
Y. Kluger
81
23
0
31 May 2020
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing
  Perspective
Performance Analysis of Plug-and-Play ADMM: A Graph Signal Processing Perspective
Stanley H. Chan
78
58
0
31 Aug 2018
The steerable graph Laplacian and its application to filtering image
  data-sets
The steerable graph Laplacian and its application to filtering image data-sets
Boris Landa
Y. Shkolnisky
74
19
0
06 Feb 2018
The Little Engine that Could: Regularization by Denoising (RED)
The Little Engine that Could: Regularization by Denoising (RED)
Yaniv Romano
Michael Elad
P. Milanfar
DiffM
119
805
0
09 Nov 2016
Algorithm-Induced Prior for Image Restoration
Algorithm-Induced Prior for Image Restoration
Stanley H. Chan
39
15
0
01 Feb 2016
Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model
  Perspective
Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective
Stanley H. Chan
Todd E. Zickler
Yue M. Lu
46
10
0
01 Jan 2016
Boosting of Image Denoising Algorithms
Boosting of Image Denoising Algorithms
Yaniv Romano
Michael Elad
104
143
0
22 Feb 2015
An algorithm for improving Non-Local Means operators via low-rank
  approximation
An algorithm for improving Non-Local Means operators via low-rank approximation
Victor May
Y. Keller
N. Sharon
Y. Shkolnisky
31
24
0
20 Nov 2014
Monte Carlo non local means: Random sampling for large-scale image
  filtering
Monte Carlo non local means: Random sampling for large-scale image filtering
Stanley H. Chan
Todd E. Zickler
Yue M. Lu
106
92
0
27 Dec 2013
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