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Proceedings Paper

Nonlocal means SAR image despeckling using Principle Neighborhood Dictionaries
Author(s): Hua Zhong; Chen Yang; L. C. Jiao
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Paper Abstract

The Principle Neighborhood Dictionary (PND) filter projects the image patches onto a lower dimensional subspace using Principle Component analysis (PCA), based on which the similarity measure of image patch can be computed with a higher accuracy for the nonlocal means (NLM) algorithm. In this paper, a new PND filter for synthetic aperture radar (SAR) image despeckling is presented, in which a new distance that adapts to the multiplicative speckle noise is derived. Compared with the commonly used Euclidean distance in NLM, the new distance measure improves the accuracy of the similarity measure of speckled patches in SAR images. The proposed method is validated on simulated and real SAR images through comparisons with other classical despeckling methods.

Paper Details

Date Published: 8 December 2011
PDF: 7 pages
Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 80021N (8 December 2011); doi: 10.1117/12.902415
Show Author Affiliations
Hua Zhong, Xidian Univ. (China)
Chen Yang, Xidian Univ. (China)
L. C. Jiao, Xidian Univ. (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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