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Wavelet Based Non-Local Means Filtering for Speckle Noise Reduction of SAR Images
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 Title & Authors
Wavelet Based Non-Local Means Filtering for Speckle Noise Reduction of SAR Images
Lee, Dea-Gun; Park, Min-Jea; Kim, Jeong-Uk; Kim, Do-Yun; Kim, Dong-Wook; Lim, Dong-Hoon;
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This paper addresses the problem of reducing the speckle noise in SAR images by wavelet transformation, using a non-local means(NLM) filter originated for Gaussian noise removal. Log-transformed SAR image makes multiplicative speckle noise additive. Thus, non-local means filtering and wavelet thresholding are used to reduce the additive noise, followed by an exponential transformation. NLM filter is an image denoising method that replaces each pixel by a weighted average of all the similarly pixels in the image. But the NLM filter takes an acceptable amount of time to perform the process for all possible pairs of pixels. This paper, also proposes an alternative strategy that uses the t-test more efficiently to eliminate pixel pairs that are dissimilar. Extensive simulations showed that the proposed filter outperforms many existing filters terms of quantitative measures such as PSNR and DSSIM as well as qualitative judgments of image quality and the computational time required to restore images.
SAR image;speckle noise;wavelet transform;non-local means filter;two sample t-test;
 Cited by
Baker, R. C. and Charlie, B. (1989). Nonlinear unstable systems, International Journal of Control, 23, 123-145.

Buades, A., Coll, B. and Morel, J. (2004). On Image Denoising Methods, Technical Report, CMLA.

Buades, A., Coll, B. and Morel, J. (2005a). A non-local algorithm for image denoising, IEEE International Conference on Computer Vision and Pattern Recognition.

Buades, A., Coll, B. and Morel, J. (2005b). Denoising image sequences does not require motion estimation, IEEE Conference on Advanced Video and Signal based Surveillance, 70-74.

Donoho, D. L. (1995). De-noising by soft thresholding, IEEE Transactions on Information Theory, 41, 613-627. crossref(new window)

Donoho, D. L. and Johnstone, I. M. (1994). Ideal spatial adaptation by Wavelet shrinkage, Biometrika, 81, 425-455. crossref(new window)

Ebrahimi, M. and Vrscay, E. R. (2008). Examining the role of scale in the context of the non-local-means filter, Lecture Notes in Computer Science, 5112, 170-181. crossref(new window)

Franceschetti, G. and Lanari, R. (1999). Synthetic Aperture Radar Processing, Electronic Engineering Systems Series, CRC Press.

Gupta, S., Chauhan, R. C. and Sexana, S. C. (2004). Wavelet-based statistical approach for speckle reduction in medical ultrasound images, Medical & Biological Engineering & Computing, 42, 189-192. crossref(new window)

Hagg, W. and Sties, M. (1994). Efficient speckle filtering of SAR images, Proceeding of IEEE International Symposium on Geoscience and Remote Sensing Symposium (IGARSS'94), Pasadena, California, USA, 2140-2142.

Lee, J. S. (1981). Speckle analysis and smoothing of synthetic aperture radar images, Computer Graphics and Image Processing, 17, 24-32. crossref(new window)

Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The Wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674-693. crossref(new window)

Mastriani, M. (2006). New Wavelet-based superresolution algorithm for speckle reduction in SAR images, IJCS, 1, 291-298.

Park, J. M., Song, W. J. and Pearlman, W. A. (1999). Speckle filtering of SAR images based on adaptive windowing, IEEE Proceedings Vision, Image and Signal Processing, 146, 191-197. crossref(new window)

Mastriani, M. and Giraldez, A. E. (2005). Smoothing of coeffcients in Wavelet domain for speckle reduction in synthetic aperture radar images, ICGST-GVIP Journal, 5, 1-8.

Sudha, S., Suresh, G. R. and Sukanesh, R. (2009). Comparative study on speckle noise suppression techniques for ultrasound images, International Journal of Engineering and Technology, 1, 1793-8236.

Tso, B. and Mather, P. M. (2009). Classification Methods for Remotely Sensed Data, CRC Press, 37-38.

Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity, IEEE Transactions on Image Processing, 13, 600-612. crossref(new window)