Anders C. Hansen: Mini-course on Compressed Sensing - Structure and Imaging, part I

This is the first of two lectures by Anders Hansen (Cambridge Univ. and UiO) on Compressed sensing - Structure and Imaging.

The above heading is the title of a new book to be published by Cambridge University Press. In these lectures I will cover some of the main issues discussed in this monograph/textbook. In particular, we will discuss how the key to the success of compressed sensing applied in imaging lies in the structure. For example images are not just sparse in an X-let expansion, they have a very specific sparsity structure in levels according to the X-let scales. Similarly, when considering Total Variation, the gradient coefficients are also highly structured. Moreover, in most realistic sampling scenarios, the sampling operator combined with any X-let transform yields a matrix with a very specific coherence structure. The key to successfully use compressed sensing is therefore to understand how to utilise these structures in an optimal way, in particular in the sampling procedure. In addition, as the coherence and sparsity structures have very particular asymptotic behaviour, the performance of compressed sensing varies greatly with dimension, and so does the optimal way of sampling. Fortunately, there is now a developed theory that can guide the user in detail on how to optimise the use of compressed sensing in inverse and imaging problems. I will cover several of the key aspects of the theory accompanied with real-world examples from Magnetic Resonance Imaging (MRI), Nuclear Magnetic Resonance (NMR), Surface Scattering, Electron Microscopy, Fluorescence Microscopy etc.  
 
Anders  C. Hansen is head of the group in Applied Functional and Harmonic Analysis within the Cambridge Centre of Analysis at DAMTP. He is  also Prof. II at the Institute of Mathematics, UiO. 
 
The second lecture in this mini-course is on May 9, 14.15-16 in Aud. 4, V. Bjerknes' house. 
 
Anders Hansen will also give a mini-course about Foundational Computational Problems in l^1 and Total Variation Regularisation on May 11 and 12.
Published Apr. 26, 2017 1:15 PM - Last modified Apr. 26, 2017 1:38 PM