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On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent
Bayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the solution.
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie
The proposed algorithms are demonstrated on several canonical problems such as image deblurring, inpainting, and denoising, where they are used for point estimation as well as for uncertainty visualisation and quantification.
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