A Nonlocal Means for Speckle Reduction of SAR Image With Multiscale-Fusion-Based Steerable Kernel Function

摘要

For the robustness of a patch-based metric, the nonlocal means method is widely applied for speckle reduction of synthetic aperture radar (SAR) images, where the similarity computed by the patch-based metric is used as weight, and weighted averaging is used to obtain the true value. However, not knowing the local spatial property, a fixed kernel (e.g., Gaussian kernel or uniform kernel) is always used to compute the weight. This is not good for the preservation of geometrical features (e.g., edges, lines, and points). In this letter, considering the characteristics of SAR imagery, a multiscale-fusion-based steerable kernel function was formed to explore the local spatial property of SAR images. In addition, by combining the kernel function with a ratio-based similarity metric designed with the distribution of the speckle's ratio, a new patch-based metric was formed and used with the nonlocal scheme for speckle reduction. In the experiments, by comparing with two state-of-the-art methods, a reasonable performance was obtained by our method, in terms of speckle reduction and detail preservation.