Proceedings Article | 30 October 2009
Proc. SPIE. 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis
KEYWORDS: Target detection, Speckle, Sensors, Synthetic aperture radar, Wavelets, Denoising, Image restoration, Distance measurement, Image filtering, Image classification
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.
