Abstract: The cosparse analysis model has been introduced recently as an
interesting alternative to the standard sparse synthesis approach. In this talk I will point to the differences between the two models and the advantages and disadvantages that the analysis framework introduces for signal and image processing. A general recipe for generating analysis algorithms from existing synthesis ones will be presented, together with theoretical guarantees for several such "converted techniques". The impact of these results on the sparse synthesis framework will be discussed as well, highlighting a new view to what seems to be already classical results in sparse approximation theory. I will conclude with some open problems still unanswered.
Raja Giryes received the B.Sc degree (Summa Cum Laude) in Computer Engineering and M.Sc degree in Computer Science from the Technion '' Israel Institute of Technology, in 2007 and 2009 respectively. Currently, he is a Ph.D. student in the Department of Computer Science in the Technion working with Prof. Michael Elad and is an Azrieli fellow.
His research interests include image processing, signal processing, sparse approximation theory and compressed sensing.