Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments

26 November 2019

Papers citing "Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments"

7 / 7 papers shown

Title
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme Belhal Karimi B. Miasojedow Eric Moulines Hoi-To Wai 63 91 0 02 Feb 2019
Residual Dense Network for Image Super-Resolution Yulun Zhang Yapeng Tian Yu Kong Bineng Zhong Y. Fu SupR 138 3,320 0 24 Feb 2018
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm Alain Durmus Eric Moulines 69 412 0 17 Jul 2015
Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection Charles-Alban Deledalle Samuel Vaiter M. Fadili Gabriel Peyré 61 119 0 06 May 2014
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm Marcelo Pereyra N. Dobigeon H. Batatia J. Tourneret 71 70 0 23 Jul 2012
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches J. Bioucas-Dias Antonio J. Plaza N. Dobigeon M. Parente Q. Du P. Gader Jocelyn Chanussot 86 2,572 0 28 Feb 2012
The Projected GSURE for Automatic Parameter Tuning in Iterative Shrinkage Methods Raja Giryes Michael Elad Yonina C. Eldar 140 130 0 21 Mar 2010