Latent variable models and factors analysis
Latent variable models and factors analysis
An Evaluation of Intrinsic Dimensionality Estimators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Universal Analytical Forms for Modeling Image Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient greedy learning of Gaussian mixture models
Neural Computation
The steerable pyramid: a flexible architecture for multi-scale derivative computation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Learning Nonlinear Image Manifolds by Global Alignment of Local Linear Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Multiscale Subbands of Photographic Images with Fields of Gaussian Scale Mixtures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Noise removal from images by projecting onto bases of principal components
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Image Restoration Using Space-Variant Gaussian Scale Mixtures in Overcomplete Pyramids
IEEE Transactions on Image Processing
Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures
IEEE Transactions on Image Processing
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
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We propose a new statistical model for image restoration in which neighborhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighborhood is obtained, thereby modeling the strongest correlations in that neighborhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techiques. However, the computation cost is very high which hampers its use for practical purposes.We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods.