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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 
 

Content
 

Volumes > Volume 08 > No. 1L

 
   

Structural Similarity Sparse Coding

PP: 363-369
Author(s)
Zhiqing Li, Weizhong Zhao, Zhixin Li,
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 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 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.

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