Multiresolution Local-Statistics Speckle Filtering Based on a Ratio Laplacian Pyramid

Speckle filtering in synthetic aperture radar (SAR) images is a key point to facilitate applicative tasks. A filter aimed at speckle reduction should energetically smooth homogeneous regions, while preserving point targets, edges, and linear features. A compromise, however, should be arranged on textured areas. In this work, a ratio Laplacian pyramid (RLP) is introduced to match the signal-dependent nature of speckle noise. Local statistics filtering is applied to the different spatial resolutions of the RLP of a speckled image. For natural scenes, each pyramid layer is characterized by an signal-to-noise ratio (SNR) increasing as resolution decreases. Thus, each filter may be adjusted to achieve adaptivity also across scales. In addition, the estimation of the local statistics driving the filter is more accurate thanks to the multiresolution framework. A complete procedure is setup, and a general formulation, in which the variance of speckle is theoretically derived at each resolution, is developed. Experiments carried out on remotely sensed optical images corrupted with synthetic speckle, as well as on true SAR images, show the potentiality of the pyramid-based approach compared with other established despeckle algorithms, in terms both of SNR improvements and of enhancement in visual quality.