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Proceedings Paper

Image segmentation and restoration using inverse diffusion equations and mathematical morphology
Author(s): Nengli Dong; Gang Jin; Hongbin Chen; Jiaguang Ma; Bo Qi
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Paper Abstract

Segmentation and restoration of highly noisy images is a very challenging problem. There are a number of methods reported in the literature, but more effort still need to be put on this problem. In this paper we describe the development and implementation of a new effective approach to segmentation and restoration of imagery with pervasive, large amplitude noise. The new approach is based on the recently developed stabilized inverse diffusion equations (SIDE) and mathematical morphology. First, we find an optimized SIDE force function. Secondly, we segment the image to several regions accurately using the SIDE method. Finally a grayscale mathematical morphological filter combined with SIDE is assigned to the initial image data in each region to suppress the noise and to restore the total image. A test study based on available database is presented, and the results so far indicate that this approach to highly noisy imagery segmentation and restoration is highly effective.

Paper Details

Date Published: 7 March 2003
PDF: 8 pages
Proc. SPIE 4883, SAR Image Analysis, Modeling, and Techniques V, (7 March 2003); doi: 10.1117/12.463168
Show Author Affiliations
Nengli Dong, Institute of Optics and Electronics (China)
Gang Jin, Institute of Optics and Electronics (China)
Hongbin Chen, Institute of Optics and Electronics (China)
Jiaguang Ma, Institute of Optics and Electronics (China)
Bo Qi, Institute of Optics and Electronics (China)


Published in SPIE Proceedings Vol. 4883:
SAR Image Analysis, Modeling, and Techniques V
Francesco Posa, Editor(s)

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