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): lecturenotes1
Instructor: Dr. Martin Benning
University of Cambridge