CORDIS Project
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This fellowship explores new computational methods for Bayesian inference in image processing, focusing on high-dimensional models. The project aims to develop algorithms that enhance image reconstruction and fusion techniques, particularly in medical imaging applications.
The aim of this fellowship proposal on Stochastic Optimisation and Simulation in Image Processing (SOSIP) is to investigate new computational methods to perform Bayesian inference in challenging inverse problems arising in statistical image processing.
Precisely, this proposal intends to explore new stochastic approximation and Markov chain Monte Carlo methods to perform Bayesian inference in high-dimensional statistical models that are not differentiable (e.g. involving l1 or total-variation re…
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