Parameter adaptive SAR image denoising method

In the traditional SAR image nonlocal means denoising algorithms, the patch similarity is measured by the accumulation of the pixel similarities, and a good denoising performance can be obtained for the additive noise model. This paper extends this idea to the multiplicative noise model for the SAR image, and improves the PPB (Probabilistic Patch-Based) algorithm under the weighted maximum likelihood estimation framework. Since the parameters setting in the PPB algorithm is complicated and it cannot adaptively get the best performance, this paper proposes a particle swarm optimization based parameter adaptive nonlocal means algorithm for SAR image denoising. Finally, experiments compared with the canonical PPB method on the real SAR image are carried out. Experiments demonstrate that the proposed method has a good performance in speckle reduction and details preservation.