A novel sparse coding model based on structural similarity

Zhiqing Li, Zhiping Shi, Xi Liu, Zhongzhi Shi
2010 2010 IEEE International Conference on Acoustics, Speech and Signal Processing  
Understanding and modeling the function of the neurons and neural systems are primary goal of systems neuroscience. 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 (SS_SC) for natural image feature extraction. The advantage for our model is to be able to preserve structural information from a scene, which
more » ... rom a scene, which human visual perception is highly adapted for. Using the proposed sparse coding model, the validity of image feature extraction is testified. Furthermore, compared with standard sparse coding (SC) model, the experimental results show that the quality of reconstructed images obtained by our method outperforms the SC method. Index Terms-Natural image, sparse coding, structural similarity, computational model, biological visual system
doi:10.1109/icassp.2010.5495707 dblp:conf/icassp/LiSLS10a fatcat:6ybejmshivdpnol7oalgyupjwe