Adaptively weighted nonlocal means and TV minimization for speckle reduction in SAR images

Abstract

Speckle noise reduction is an important issue in synthetic aperture radar (SAR) imaging. Because SAR images are distinct in being complex valued and susceptible to corruption owing to multiplicative fluctuations, specialized methods for speckle reduction are needed. Techniques based on nonlocal means perform denoising by exploiting the natural redundancy of patterns within an image. They calculate a weighted average of pixels whose neighborhoods are close to one another, where this significantly reduces noise while preserving most image content. While this method performs well on flat areas and textures, its results are excessively smooth in low-contrast areas, and leave residual noise around edges and singular structures. Another variational denoising method uses total variation (TV) minimization to restore regular images but is prone to excessively smooth textures, the staircasing effect, and contrast losses. Our proposed model is intended for the logarithmic domain of SAR data, and combines the above two methods by minimizing an adaptive TV using a nonlocal data fidelity term. In the variational functionals developed here, weighted parameters of nonlocal regularization are adaptively tuned based on local heterogeneity information and noise in the images. A fast iterative shrinkage/thresholding algorithm (FISTA) is then used to solve the optimization problem. The results of experiments on real SAR images verify the effectiveness of the proposed method in terms of speckle reduction.