Structural Similarity Sparse Coding

Abstract

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 co ding 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 perception is highly adapted for. Using the proposed sparse coding model, the validity of image patch feature extraction is testified. Furthermore, compared with standard sparse coding model, the experimental results show that the quality of reconstructed images obtained by our method outperforms standard sparse coding model.