Minicourse: Modern Regularization Methods for Inverse Problems

Martin Benning - University of Cambridge

Date and time
Monday, May 28, 2018 at 10:30 AM

Contact person
Giacomo Albi

Publication date
March 1, 2018

Computer Science  


Regularization methods are a key tool in the solution of inverse problems. They are used to introduce prior knowledge and make the approximation of ill-posed (pseudo)inverses feasible. We will discuss variational methods and techniques derived from those, since they have attracted particular interest in the last years and link to other fields like image processing and compressed sensing. We further point to developments related to statistical inverse problems, multiscale decompositions, and learning theory. 

The course will be divided in 4 blocks: 
Block 1 (28/05): Linear inverse problems & regularisation
Block 2 (29/05): Variational regularisation methods
Block 3 (30/05): Iterative regularisation & applications
Block 4 (31/05): Parameter identification/Machine learning

For a detailed timetable: 

Monday        28/05,    11:30-13:30, Aula G.

Tuesday        29/05,     8:30-10:30,  Aula G.

Wednesday  30/05,     8:30-10:30,  Aula E.

Thursday       31/05,     14:30-16:30, Aula G.

Keywords: Regularization, Inverse Problems, Image Reconstruction, Variational Methods, Bregman Iteration, Convergence, Error Estimation
Material (extened): lecturenotes1lecturenotes2

Instructor: Dr. Martin Benning
University of Cambridge


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