Image Denoising Using Similarities in the Time-Scale Plane
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Image denoising in steerable pyramid domain based on a local Laplace prior
Pattern Recognition
Image denoising using mixtures of projected Gaussian scale mixtures
IEEE Transactions on Image Processing
A SURE approach for digital signal/image deconvolution problems
IEEE Transactions on Signal Processing
Image restoration through L0 analysis-based sparse optimization in tight frames
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image restoration by mixture modelling of an overcomplete linear representation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Complex Gaussian scale mixtures of complex wavelet coefficients
IEEE Transactions on Signal Processing
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Computers and Electrical Engineering
Time-Scale Similarities for Robust Image De-noising
Journal of Mathematical Imaging and Vision
Edge structure preserving image denoising using OAGSM/NC statistical model
Digital Signal Processing
Perceptually optimized blind repair of natural images
Image Communication
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In recent years, Bayes least squares-Gaussian scale mixtures (BLS-GSM) has emerged as one of the most powerful methods for image restoration. Its strength relies on providing a simple and, yet, very effective local statistical description of oriented pyramid coefficient neighborhoods via a GSM vector. This can be viewed as a fine adaptation of the model to the signal variance at each scale, orientation, and spatial location. Here, we present an enhancement of the model by introducing a coarser adaptation level, where a larger neighborhood is used to estimate the local signal covariance within every subband. We formulate our model as a BLS estimator using space-variant GSM. The model can be also applied to image deconvolution, by first performing a global blur compensation, and then doing local adaptive denoising. We demonstrate through simulations that the proposed method, besides being model-based and noniterative, it is also robust and efficient. Its performance, measured visually and in L2-norm terms, is significantly higher than the original BLS-GSM method, both for denoising and deconvolution.