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» Principal components for non-local means image denoising
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CIMAGING
2008
172views Hardware» more  CIMAGING 2008»
13 years 7 months ago
A generalization of non-local means via kernel regression
The Non-Local Means (NLM) method of denoising has received considerable attention in the image processing community due to its performance, despite its simplicity. In this paper, ...
Priyam Chatterjee, Peyman Milanfar
ICCV
2009
IEEE
1318views Computer Vision» more  ICCV 2009»
14 years 10 months ago
Non-Local Sparse Models for Image Restoration
We propose in this paper to unify two different ap- proaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has p...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
ICIP
2009
IEEE
14 years 6 months ago
Non-local Image Smoothing By Applying Anisotropic Diffusion Pde's In The Space Of Patches
We design a family of non-local image smoothing algorithms which approximate the application of diffusion PDE's on a specific Euclidean space of image patches. We first map a...
PR
2010
314views more  PR 2010»
13 years 3 months ago
Two-stage image denoising by principal component analysis with local pixel grouping
Lei Zhang 0006, Weisheng Dong, David Zhang, Guangm...
WSCG
2004
166views more  WSCG 2004»
13 years 6 months ago
De-noising and Recovering Images Based on Kernel PCA Theory
Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis ar...
Pengcheng Xi, Tao Xu