Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior

By Mário A. T. Figueiredo, Robert D. Nowak. 2001

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Citations in the corpus

Density Modeling of Images using a Generalized Normalization Transformation (2015, methodology): “For comparison, we implemented two denoising methods that operate on orthogonal wavelet coef-ﬁcients, one assuming a marginal model (Figueiredo & Nowak, 2001), and the other an elliptically symmetric Gaussian scale mixture (GSM) model (Portilla et al., 2003).”
Image denoising using scale mixtures of Gaussians in the wavelet domain (2003, methodology): “Such solutions have been used in establishing marginal priors for image denoising [45].”
Adaptive Sparseness for Supervised Learning (2003, background): “Of course, this prior no longer leads to a Laplacian prior on � , but to some other prior (see [ 19 ] for details).”
Iterative Gaussianization: From ICA to Random Rotations (2011, methodology): “Many methods have exploited the Bayesian framework to this end [10], [46]– [48]:”
Directionlets: anisotropic multidirectional representation with separable filtering (2006, methodology): “The most common solution is known as Jeffrey’s prior and it has also been used in [39] to define marginal priors for image denoising.”
Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis (2011, methodology): “To approximate the signal term, we threshold the image stack with an Amplitude-scale-invariant Bayes Estimator (ABE) using Jeffreys’ non-informative prior [70, 71, 72] as an estimate of the significance of wavelet coefficient W I i (x,y) at a given scale i and position (x,y) W I i (x,y)≈ δ ABE (Wi(x”
Sparse Reconstruction by Separable Approximation (2008, background): “In wavelet-based image restoration, the regularizer c is often the p-th power of an ‘p norm, resulting from adopting generalized Gaussian priors for the wavelet coefficients of natural images [60], although other regularizers have been considered (e.g., [ 35 ], [43], [44]).”
On the Role of Sparse and Redundant Representations in Image Processing (2010, methodology): “significantly reduce these artifacts and produce a substantial improvement in the denoising outcome [14], [28], [37].”
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency (2002, methodology): “simple bivariate shrinkage functions can also be developed, for example, a bivariate hard threshold with a circular or ellipsoidal deadzone, or a bivariate generalization of the semi-soft rule of [17] and [18].”