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Proceedings Paper

Infrared image denoising algorithm based on adaptive dictionary learning
Author(s): Deqin Shi; Wei Yang; Junshan Li
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Paper Abstract

A novel infrared image denosing algorithm is proposed based on adaptive dictionary learning over sparse and redundant representations. The dictionary which can yield sparse representations is learned from the corrupted infrared image itself, instead of using the prechosen set of basis functions such as curvelet or contourlet. Meanwhile, the over-completed dictionary is updated adaptively in the online learning procedure other than batch learning method to improve the learning performance. And the learning and denoising procedure are fused together into one iterated process naturally and properly. Experimental results demonstrate the effectiveness of the denosing algorithm for infrared images.

Paper Details

Date Published: 8 December 2011
PDF: 5 pages
Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 80021R (8 December 2011); doi: 10.1117/12.902876
Show Author Affiliations
Deqin Shi, Xi'an Research Institute of High Technology (China)
Wei Yang, Xi'an Research Institute of High Technology (China)
Junshan Li, Xi'an Research Institute of High Technology (China)


Published in SPIE Proceedings Vol. 8002:
MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis
Faxiong Zhang; Faxiong Zhang, Editor(s)

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