Maximum likelihood orthogonaldictionary learning

Dictionary learning algorithms have received widespread acceptance when it comes to data analysis and signal representations problems. These algorithms consist of two stages: the sparse coding stage and dictionary update stage. This latter stage can be achieved sequentially or in parallel. In this work, the maximum likelihood approach is used to derive a new approach to dictionary learning. The proposed method differs from recent dictionary learning algorithms for sparse representation by updating all the dictionary atoms in parallel using only one eigen-decomposition. The effectiveness of the proposed method is tested on two different image processing applications: filling-in missing pixels and noise removal.