This course reviews the applications of sparse representations in image processing, with an emphasis on the compressed sensing method. It alternates between the exposition of the theory and a practical implementation of the methods.
Sparsity has recently emerged as a fundamental tool in image processing. It allows one to take into account the compressibility of images in a well chosen representation. It leads to state of the art methods to regularize inverse problems such as super-resolution, medical imaging and astrophysical imaging. It is also at the heart of compressed sensing, a revolutionary method to sample data in an already compressed form.
Pre-requisite: basics of linear algebra, calculus and Fourier transform.
Validation of the course: attending all the numerical tours (5pts/20), a mini-project with a report (5pts/20) and an oral presentation (10pts/20).
Note that there is a special session dedicated to the preparation of the projects.