Mini-Course 4 - Computational imaging & learning: from physics to data-driven methods

Course Instructor:  Prof. Luca Calatroni - Università di Genova 

Course Title: Computational imaging & learning: from physics to data-driven methods

When:  10 - 15 May

Content(sketch):

The school introduces the mathematical and computational foundations of computational imaging, emphasizing the modeling, formulation, and solution of inverse problems in modern imaging systems. It begins with physical models of image acquisition in contexts such as microscopy and tomography, which are discretized into ill-posed inverse problems. To address these, the course explores regularization and Bayesian methods like maximum a posteriori (MAP) estimation. Optimization techniques for image reconstruction, including gradient-based and proximal algorithms, are then presented. The course concludes with data-driven and learning-based methods that integrate with model-based approaches to enhance performance and interpretability.