MCMC and Variational Approaches for Bayesian Inversion in Diffraction Imaging

Ayasso, Hacheme; Duchêne, Bernard; Mohammad-Djafari, Ali

doi:10.1002/9781118827253.ch8

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can be equal to zero or any other prior default solution. Different choices for these two terms and a great number of optimization algorithms have been proposed with success in very diversified domains and applications [ 1 , 2 , 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems

Mohammad-Djafari

2021

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Classical methods for inverse problems are mainly based on regularization theory, in particular those, that are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and a great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond to the likelihood and prior-probability models, respectively. The Bayesian approach gives more flexibility in choosing these terms and, in particular, the prior term via hierarchical models and hidden variables. However, the Bayesian computations can become very heavy computationally. The machine learning (ML) methods such as classification, clustering, segmentation, and regression, based on neural networks (NN) and particularly convolutional NN, deep NN, physics-informed neural networks, etc. can become helpful to obtain approximate practical solutions to inverse problems. In this tutorial article, particular examples of image denoising, image restoration, and computed-tomography (CT) image reconstruction will illustrate this cooperation between ML and inversion.

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Section: Introductionmentioning

confidence: 99%