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Robust priors for smoothing and image restoration
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/0471725382openalex-snapshot
Robust Regression and Outlier Detection
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On the choice of regularisation parameter in image restoration
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A Monte Carlo method for an objective Bayesian procedure
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Constructing simple stable descriptions for image partitioning
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Bayesian image restoration, with two applications in spatial statistics
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Likelihood and the Bayes procedure
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Introduction to matrix analysis
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Digital Image Processing
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Robust Regression and Outlier Detection
inferredcites →https://doi.org/10.1080/01621459.1987.10478534openalex-snapshot
Non-Gaussian State-Space Modeling of Nonstationary Time Series
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Towards Bayesian image analysis
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Constrained restoration and the recovery of discontinuities
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Bayesian reconstructions from emission tomography data using a modified EM algorithm
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
inferredcites →https://doi.org/10.1111/j.2517-6161.1986.tb01412.xopenalex-snapshot
On the Statistical Analysis of Dirty Pictures
inferredcites →https://doi.org/10.1111/j.2517-6161.1986.tb01417.xopenalex-snapshot
On Some Smoothing Techniques Used in Image Restoration
inferredcites →https://doi.org/10.1137/1.9781611970128openalex-snapshot
Spline Models for Observational Data
inferredcites →https://doi.org/10.1214/aos/1176349743openalex-snapshot
A Comparison of GCV and GML for Choosing the Smoothing Parameter in the Generalized Spline Smoothing Problem
inferredcites →https://doi.org/10.2307/1427013openalex-snapshot
Relations between Models for Spatial Data, Contingency Tables and Markov Fields on Graphs
inferredcites →https://doi.org/10.2307/2290434openalex-snapshot
Spline Models for Observational Data.
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Wavelet‐Based Image Deconvolution and Reconstruction
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Impact of the non‐negativity constraint in model‐based iterative reconstruction from CT data
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A Comparison of Estimation Methods for Hydraulic Conductivity Functions from Field Data
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A Comparison Between L1 Markov Random Field-based and Wavelet-based Estimators
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Evaluating a General Class of Filters for Image Denoising
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Spatial Smoothing with Robust Priors in Functional MRI
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Non-quadratic convex regularized reconstruction of MR images from spiral acquisitions
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On the use of Gibbs priors for Bayesian image restoration
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Super-resolution in map-making based on a physical instrument model and regularized inversion
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Edge-Preserving Smoothers for Image Processing
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Properties of Designs in Experiments on Spatial Lattices
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Smoothers for Discontinuous Signals
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A regularization by denoising (RED) scheme for 3-D FWI model updates in large-contrast media
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Thresholding implied by truncated quadratic regularization
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Regularized estimation of mixed spectra using a circular Gibbs-Markov model
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Nonlinear image recovery with half-quadratic regularization
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Regularized Doppler radar imaging for target identification in atmospheric clutter
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Regularisation functions and estimators
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Con-Net: A Consolidated Light-Weight Image Restoration Network
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A Full-Waveform Inversion Method Based on Structural Tensor Constraints
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Stochastic nonlinear image restoration using the wavelet transform
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Unsupervised Bayesian Convex Deconvolution Based on a Field With an Explicit Partition Function
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Convergence Analysis of Generalized Iteratively Reweighted Least Squares Algorithms on Convex Function Spaces
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Complexity Penalized M-Estimation
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Unsupervised empirical Bayesian multiple testing with external covariates
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Locally adaptive regression splines
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Local Extremes, Runs, Strings and Multiresolution
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Bayesian Computation and Stochastic Systems
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Mistral: a myopic edge-preserving image restoration method, with application to astronomical adaptive-optics-corrected long-exposure images
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Bayesian estimation of regularization and point spread function parameters for Wiener–Hunt deconvolution
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Degrees of freedom for off-the-grid sparse estimation
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Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
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Segmentation of Time Series: Parameter Dependence of Blake-Zisserman and Mumford-Shah Functionals and the Transition from Discrete to Continuous
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Characteristics of the Linear Smoothing Spline.
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Bayesian mapping of brain regions using compound Markov random field priors
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RPCA with Log-Schatten Norm and Adaptive Histogram Equalization for Medical Imaging
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