Ratio-based nonlocal anisotropic despeckling approach for sar images

Although the first filtering algorithms have been proposed more than 30 years ago, despeckling of synthetic aperture radar images is still an open issue. A new boost has been provided by nonlocal (NL) means filters. The idea of NL filters is to move from the exploitation of spatial neighboring pixels to the exploitation of similar pixels found across the image. The difference between the NL algorithms is mainly related to the definition of the similarity between pixels and how similar pixels are exploited in the restoration process. Generally, to define the similarity, the patches are adopted. In this paper, a new similarity criterion for selecting similar pixels is presented. It is based on the definition of the ratio patch between the patch containing the pixel to be restored and the patch containing a candidate similar pixel. If the two pixels are similar, it is expected that the corresponding ratio patch will follow a specific statistical distribution. A modified version of the Kolmogorov-Smirnov distance is introduced to decide whether the statistical distribution of the ratio patch follows the expected one. To reduce the possible artifacts, anisotropy is exploited. Considering the proposed approach, the designed algorithm turns to be unbiased, able to provide the restored solution without any thresholding procedure, in which the tuning is substantially unsupervised and able to work with both single-look and multilook images. The algorithm has been tested on different simulated and real data. Qualitative and quantitative analyses validate the proposed approach, showing very good despeckling capabilities.