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Proceedings Paper

Remotely sensed image restoration using partial differential equations and watershed transformation
Author(s): Avishan Nazari; Amin Zehtabian; Marco Gribaudo; Hassan Ghassemian
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Paper Abstract

This paper proposes a novel approach for remotely sensed image restoration. The main goal of this study is to mitigate two most well-known types of noises from remote sensing images while their important details such as edges are preserved. To this end, a novel method based on partial differential equations is proposed. The parameters used in the proposed algorithm are adaptively set regarding the type of noise and the texture of noisy datasets. Moreover, we propose to apply a segmentation pre-processing step based on Watershed transformation to localize the denoising process. The performance of the restoration techniques is measured using PSNR criterion. For further assessment, we also feed the original/noisy/denoised images into SVM classifier and explore the results.

Paper Details

Date Published: 14 February 2015
PDF: 5 pages
Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 944520 (14 February 2015); doi: 10.1117/12.2181817
Show Author Affiliations
Avishan Nazari, Politecnico di Milano (Italy)
Amin Zehtabian, Tarbiat Modares Univ. (Iran, Islamic Republic of)
Marco Gribaudo, Politecnico di Milano (Italy)
Hassan Ghassemian, Tarbiat Modares Univ. (Iran, Islamic Republic of)

Published in SPIE Proceedings Vol. 9445:
Seventh International Conference on Machine Vision (ICMV 2014)
Antanas Verikas; Branislav Vuksanovic; Petia Radeva; Jianhong Zhou, Editor(s)

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