Image reconstruction using NMF with sparse constraints based on kurtosis measurement criterion

Authors:
Li Shang;Jinfeng Zhang;Wenjun Huai;Jie Chen;Jixiang Du
Affiliations:
JiangSu Province Support Software Eng. R&D Center for Modern Information Technology Application in Enterprise, Suzhou, Jiangsu, China and Dept. of Electronic Information Engineering, Suzhou Vocati ...;Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou, Jiangsu, China;Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou, Jiangsu, China;Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou, Jiangsu, China;Dept. of Comp. Sci. and Techn., Huaqiao Univ., Quanzhou, Fujian, China and Dept. of Automation, Univ. of Science and Techn. of China, Hefei, Anhui, China and Institute of Intelligent Machines, Chi ...
Venue:
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

A novel image reconstruction method using non-negative matrix factorization (NMF) with sparse constraints based on the kurtosis measurement is proposed by us. This NMF algorithm with sparse constraints exploited the Kurtosis as the maximizing sparse measure criterion of feature coefficients. The experimental results show that the natural images' feature basis vectors can be successfully extracted by using our algorithm. Furthermore, compared with the standard NMF method, the simulation results show that our algorithm is indeed efficient and effective in performing image reconstruction task.