Image denoising using mixtures of projected Gaussian scale mixtures
IEEE Transactions on Image Processing
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
An affine symmetric image model and its applications
IEEE Transactions on Image Processing
Complex Gaussian scale mixtures of complex wavelet coefficients
IEEE Transactions on Signal Processing
Journal of Mathematical Imaging and Vision
Transformation equivariant Boltzmann machines
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Computers and Electrical Engineering
Edge structure preserving image denoising using OAGSM/NC statistical model
Digital Signal Processing
Image denoising using SVM classification in nonsubsampled contourlet transform domain
Information Sciences: an International Journal
A New Poisson Noise Filter Based on Weights Optimization
Journal of Scientific Computing
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We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation. Neighborhoods are modeled as samples of a multivariate Gaussian density that are modulated and rotated according to the values of two hidden random variables, thus allowing the model to adapt to the local amplitude and orientation of the signal. A third hidden variable selects between this oriented process and a nonoriented scale mixture of Gaussians process, thus providing adaptability to the local orientedness of the signal. Based on this model, we develop an optimal Bayesian least squares estimator for denoising images and show through simulations that the resulting method exhibits significant improvement over previously published results obtained with Gaussian scale mixtures.