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2013 IEEE International Conference on Multimedia and Expo (ICME) (2013)
San Jose, CA, USA
July 15, 2013 to July 19, 2013
ISSN: 1945-7871
ISBN: 978-1-4799-0015-2
pp: 1-6
Na Qi , Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, China
Yunhui Shi , Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, China
Xiaoyan Sun , Microsoft Research Asia, Beijing, China
Jingdong Wang , Microsoft Research Asia, Beijing, China
Baocai Yin , Beijing Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, China
ABSTRACT
Sparse representation has been proved to be very efficient in machine learning and image processing. Traditional image sparse representation formulates an image into a one dimensional (1D) vector which is then represented by a sparse linear combination of the basis atoms from a dictionary. This 1D representation ignores the local spatial correlation inside one image. In this paper, we propose a two dimensional (2D) sparse model to much efficiently exploit the horizontal and vertical features which are represented by two dictionaries simultaneously. The corresponding sparse coding and dictionary learning algorithm are also presented in this paper. The 2D synthesis model is further evaluated in image denoising. Experimental results demonstrate our 2D synthesis sparse model outperforms the state-of-the-art 1D model in terms of both objective and subjective qualities.
INDEX TERMS
Dictionaries, Vectors, Sparse matrices, Complexity theory, Training, Correlation, Image denoising
CITATION

Na Qi, Yunhui Shi, Xiaoyan Sun, Jingdong Wang and Baocai Yin, "Two dimensional synthesis sparse model," 2013 IEEE International Conference on Multimedia and Expo (ICME), San Jose, CA, USA USA, 2013, pp. 1-6.
doi:10.1109/ICME.2013.6607508
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