Nonlinear despeckle filtering

In this chapter, we will review some of the methods proposed in literature to remove the speckle pattern from ultrasound data based on nonlinear processing. Note that some filters that can also be considered as nonlinear are left aside since they will be deeply treated in other chapters. That is the case of the wavelet-based methods and Bayesian methods. Other methods also treated in other chapters, like diffusion-based schemes are only briefly reviewed in order to place them inside the global partial differential equation (PDE) classification. On the other hand, note that some of the filters here reviewed are not initially proposed for ultrasound imaging but derived for Synthetic Aperture Radar (SAR) images, where noise can be modeled similarly. In those images, the multiplicative model for speckle holds and therefore, many of the methods defined in literature for SAR can be easily extrapolated to ultrasound denoising. This is the case of some of the most popular speckle filters. In addition, we would like to remark that the effectiveness of many of these schemes lays on a proper modeling of the speckle statistics. For some purposes, a simple multiplicative model will suffice, while for some specific applications, more accurate models must be used. Finally, as we stated in the previous chapter, the filtering method must be selected following the specific needs of the problem. There is no all-purpose filter that, with the same configuration parameters, could perform excellent in all situations.