Classification based nonlocal means despeckling for SAR image

Hua Zhong; Jingjing Xu; Licheng Jiao

doi:10.1117/12.832169

30 October 2009 Classification based nonlocal means despeckling for SAR image

Hua Zhong, Jingjing Xu, Licheng Jiao

Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74950V (2009) https://doi.org/10.1117/12.832169
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

The nonlocal (NL) means filter as a recent denoising approach has demonstrated its empirical merit for additive Gaussian noise. In this paper, a new nonlocal means despeckling method for synthetic aperture radar (SAR) image is proposed, which is adapted to the multiplicative model of speckle noise. The proposed method still uses Euclidean distance based similarity measure but adopting a strategy of pixel classification, which can effectively reduce the influence of the multiplicative speckle model and improve the effectiveness in searching of similar patches, thus contributes to the final results. By this strategy, image pixels are first classified into different classes such as point, line, edge, etc., and then different smooth parameters of nonlocal means filter are used according to the class information. In addition, a searching method for rotation-invariant similar patches is designed through the use of directional information. We validate the proposed method on real synthetic aperture radar (SAR) images and confirm the excellent despeckling performance through comparisons with other classical despeckling methods, such the Enhanced Lee filter, Enhanced Gamma MAP filter, wavelet thresholding, as well as original NL mean filter.

Citation Download Citation

Hua Zhong, Jingjing Xu, and Licheng Jiao "Classification based nonlocal means despeckling for SAR image", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74950V (30 October 2009); https://doi.org/10.1117/12.832169

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