Dictionary design for sparse signal representations using K-SVD with sparse Bayesian learning

Sparse representations of signals in overcomplete basis have attracted much interest during the past two decades. One problem in the area of sparse signal representations is to find an ideal overcomplete basis (dictionary) to represent a given set of training signals appropriately. The K-SVD algorithm has achieved this feat with much success but suffers from the problem of underutilization of certain signal-atoms in the basis. This paper...