Abstract
This paper introduces a novel algorithm that combines the Non-Subsampled Contourlet Transform (NSCT) and morphological operators to reduce the multiplicative noise of synthetic aperture radar images. The image corrupted by multiplicative noise is preprocessed and decomposed into several scales and directions using the NSCT. Then, the contours and uniform regions of each subband are separated from noise. Finally, the resulting denoised subbands are transformed back into the spatial domain and applied the exponential function to obtain the denoised image. Experimental results show that the proposed method drastically reduces the multiplicative noise and outperforms other denoising methods, while achieving a better preservation of the visual details.
This work was supported by FOMIX CHIH-2009-C01-117569.
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Mejía Muñoz, J.M. et al. (2010). SAR Image Denoising Using the Non-Subsampled Contourlet Transform and Morphological Operators. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Artificial Intelligence. MICAI 2010. Lecture Notes in Computer Science(), vol 6437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16761-4_30
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DOI: https://doi.org/10.1007/978-3-642-16761-4_30
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