Institute of Information Theory and Automation

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Bayesian methods for non-linear blind inverse problems

Agency: 
GACR
Identification Code: 
GA20-27939S
Start: 
2020-01-01
End: 
2022-12-31
Project Focus: 
teoretický
Project Type (EU): 
other
Abstract: 
Blind inverse problems (i.e. inverse problems with unknown parameters of the forward model) are well studied for models with uniform grids, such as blind image deconvolution or blind signal separation. Recently, new methods of learning of non-linear problems with differentiable nonlinearities (i.e. neural networks) have been proposed, however they rely on supervised learning on a training set. The aim of this project is to develop methods of blind inverse problems with a general observation operator (such as irregular grids) and general parametric structure that can be learned from a single sample. The main challenge is to find suitable regularization structure. We propose to use the Bayesian approach defining regularization using prior distribution and novel methods of distribution representation developed in machine learning such as variational autoencoders. The proposed methods will be validated in specialized models for: i) determination of a source term of an atmospheric release, and ii) adaptive measurement grid refinement in environmental electron microscopy.
Publications ÚTIA: 
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2020-04-20 12:40