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Proceedings Paper

Improved de-noising method based on spare representation for remote sensing image
Author(s): Delin Mo; Shuai Xing; Qin Xia; Tengda Jiang; Junjun Zhang; Zhongxiao Ge
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Paper Abstract

Remote sensing satellite image de-noising is an important step in image preprocessing. Four de-noising algorithms for remote sensing images are investigated in this paper: BM3D, DCT, K-SVD, and wavelet threshold method. A modified method based on K-SVD is also proposed. The basic principles of the four kinds of de-noising methods are introduced, and the modified method is analyzed thoroughly. In the improved method, high-frequency information is extracted through High-pass filtering, and then sparse representation and reconstruction are carried out to maintain the detail information. Comparative experiments are conducted to reveal the advantages and disadvantages of each method in satellite images de-noising, and the results demonstrate that the proposed method can get better de-noising result as well as keeping the details at the same time.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331T (29 August 2016); doi: 10.1117/12.2244882
Show Author Affiliations
Delin Mo, Zhengzhou Institute of Surveying and Mapping (China)
Shuai Xing, Zhengzhou Institute of Surveying and Mapping (China)
Qin Xia, Zhengzhou Institute of Surveying and Mapping (China)
Tengda Jiang, Zhengzhou Institute of Surveying and Mapping (China)
Junjun Zhang, Zhengzhou Institute of Surveying and Mapping (China)
Zhongxiao Ge, Zhengzhou Institute of Surveying and Mapping (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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