Learning Convex Regularizers for Optimal Bayesian Denoising

16 May 2017 Ha Q. Nguyen Emrah Bostan Michael Unser

We propose a data-driven algorithm for the maximum a posteriori (MAP) estimation of stochastic processes from noisy observations. The primary statistical properties of the sought signal is specified by the penalty function (i.e., negative logarithm of the prior probability density function)... (read more)

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