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Keywords
(10)
Covariance Matrices
Finite Mixture
Gaussian Noise
image denoising
Indexing Terms
mixture of gaussians
Noise Removal
Power Modeling
Statistical Properties
Gaussian Scale Mixture
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Image denoising using mixtures of Gaussian scale mixtures
Image denoising using mixtures of Gaussian scale mixtures,10.1109/ICIP.2008.4711817,Jose A. Guerrerocolon,Eero P. Simoncelli,Javier Portilla
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Image denoising using mixtures of Gaussian scale mixtures
(
Citations: 8
)
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Jose A. Guerrerocolon
,
Eero P. Simoncelli
,
Javier Portilla
The local
statistical properties
of photographic images, when represented in a multiscale basis, have been described using Gaussian scale mixtures (GSMs). In that model, each spatial neighborhood of coefficients is described as a Gaussian random vector modulated by a random hidden positive scaling variable. Here, we introduce a more powerful model in which neighborhoods of each subband are described as a
finite mixture
of GSMs. We develop methods to learn the mixing densities and
covariance matrices
associated with each of the GSM components from a single image, and show that this process naturally segments the image into regions of similar content. The model parameters can also be learned in the presence of additive Gaussian noise, and the resulting fitted model may be used as a prior for Bayesian noise removal. Simulations demonstrate this model substantially outperforms the original GSM model.
Conference:
Image Processing, IEEE International Conference  ICIP
, pp. 565568, 2008
DOI:
10.1109/ICIP.2008.4711817
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Citation Context
(7)
...The Gaussian scale mixture (GSM) model and its expansion has been shown to produce results that are significantly better than HMM models [4][
1213
]...
XiangYang Wang
,
et al.
A new image denoising in shiftable complex directional pyramid domain
...In this sense, the paper further builds upon the MGSM model from [18], [22], [
25
] and is also a continuation of our previous work in [26]...
...This approach combines the GSM model with a Markov Random Field model and currently yields better denoising performance on average than MGSM (see [
25
], [33])...
...4Very recently, in parallel to our research, in [
25
] a similar result is obtained for the noisefree House image...
...As noted in [
25
], underutilized mixture components may attribute to a large part of the computation time...
...MGSM model from [
25
] when we chooseq = d and identity matrices for the projection bases Vk = I. This is equivalent to not incorporating dimension reductions into the model, hence the third layer in Fig. 1 is missing...
Bart Goossens
,
et al.
Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures
...There has been significant prior work in describing the statistics of wavelet coefficients for a single image [1] [
2
] . A Gaussian scale mixture (GSM) distribution has been shown to be a good model for describing the statistics of wavelet coefficients for single images [3]...
Rajiv Soundararajan
,
et al.
Statistical modeling of multicamera images
...What about nonlinear image models? Apart from the nonparametric approaches, a large number of nonlinear image models has been proposed over the years which are capable to capture signicantly more statistical regularities of natural images than linear ICA can do (e.g. [59; 60; 56; 61; 62; 63; 64;
65
; 66])...
Jan Eichhorn
,
et al.
Natural Image Coding in V1: How Much Use is Orientation Selectivity?
...Some of the most recent and successful advances are based on: Gaussian scale mixtures (GSM) modeling in overcomplete multiscale transform domain [
9
], [15], [8]; learned dictionaries of atoms to Þlter small square neighborhoods [6]; steering kernel regression [16] (also combined with learned dictionaries [2]); shapeadaptive DCT (SADCT) on neighborhoods whose shapes are adaptive to the image structures [7]; nonlocal Þltering ...
Kostadin Dabov
,
et al.
BM3D Image Denoising with ShapeAdaptive Principal Component Analysis
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Citations
(8)
A new image denoising in shiftable complex directional pyramid domain
(
Citations: 1
)
XiangYang Wang
,
TianXiang Qu
Conference:
International Conference on Advanced Computer Control  ICACC
, 2010
Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures
(
Citations: 3
)
Bart Goossens
,
Aleksandra Pizurica
,
Wilfried Philips
Journal:
IEEE Transactions on Image Processing
, vol. 18, no. 8, pp. 16891702, 2009
Statistical modeling of multicamera images
Rajiv Soundararajan
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Alan C. Bovik
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Sriram Vishwanath
Conference:
Asilomar Conference on Signals, Systems & Computers  ASILOMAR
, pp. 351355, 2009
Natural Image Coding in V1: How Much Use Is Orientation Selectivity?
Jan Eichhorn
,
Fabian Sinz
,
Matthias Bethge
Journal:
PLOS Computational Biology  PLOS COMPUT BIOL
, vol. 5, no. 4, 2009
Natural Image Coding in V1: How Much Use is Orientation Selectivity?
(
Citations: 8
)
Jan Eichhorn
,
Fabian Sinz
,
Matthias Bethge
Journal:
PLOS Computational Biology  PLOS COMPUT BIOL
, vol. 5, no. 4, 2008