Sparse modelling calls for constructing efficient representations of data as a combination of a few typical patterns (atoms) learned from the data itself. Significant contributions to the theory and practice of learning such collections of atoms (usually called Dictionaries), and of representing the actual data in terms of them, have been made thereby leading to state-of-the-art results in many...

Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics. In this paper, we propose a novel sparse coding model based on structural similarity for natural image patch feature extraction. The advantage for our model is to be able to preserve structural information from a scene, which human visual per...