NDLI: Image compressed sensing based on multi-level adaptive learning dictionaries

Content Provider	IEEE Xplore Digital Library
Author	Qicong Wang Meixiang Zhang Yunqi Lei Yehu Shen
Copyright Year	2014
Description	Author affiliation: Dept. of Syst. Integration & IC Design, Suzhou Inst. of Nano-Tech & Nano-Bionics, Suzhou, China (Yehu Shen) \|\| Dept. of Comput. Sci., Xiamen Univ., Xiamen, China (Qicong Wang; Meixiang Zhang; Yunqi Lei)
Abstract	Sparse representation matrix is of great significance for Compressed Sensing (CS) reconstruction accuracy. Contourlet Transform (CT) offer a much richer set of directions and shapes, and it is more effective in capturing smooth contours and geometric structures in images. While dictionaries learned by machine learning methods can represent images more effectively. In this paper, we propose a multi-level adaptive dictionary learning (DL) strategy which combines both of the above advantages. We learn sub-dictionaries of high frequency of CT by an improved K-SVD algorithm, and moreover, the stopping criteria of sparse representation stage is associated with the iteratively updated dictionaries to get an adaptive sparse constraint, which gets more effective sparse representation coefficients and then improves the dictionary updating. This approach achieves a good reconstruction accuracy of the high frequency with less CS measurement. Experiment results demonstrate that the reconstructed images using dictionaries learned by the proposed algorithm in CS have better effect.
Starting Page	782
Ending Page	787
File Size	916228
Page Count	6
File Format	PDF
e-ISBN	9781479951482
DOI	10.1109/FSKD.2014.6980936
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Dictionaries compressed sensing dictionary Learning Transforms K-SVD Sparse matrices Image reconstruction Compressed sensing contourlet transform
Content Type	Text
Resource Type	Article

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