The aim of this course is to present the problem of image reconstruction which is frequently encountered in a large number of imaging applications such as biomedical, satellite and seismic imaging. The study of this type of problems requires the use of advanced notions of image processing such as sparse reconstruction, anisotropic and non-linear diffusion-type PDEs, representation in wavelet bases, non-smooth optimization etc. The methodology described further involves the use of tools frequently encountered in general high-dimensional data processing and often used in several model and variable selection problems, as well as the design of smooth and non-smooth optimization algorithms (e.g. proximal gradient type) for which a parallel with deep learning methods will be made.
AI PhD Curriculum