Software framework for visualizing space-variant image filters

We have developed a software framework that simplifies the task of implementing, controlling, and visualizing space-variant image filters. A filter's behaviour over an image is dictated by the parameters that control it. The values of each parameter can be data, geometric, algorithmic, or user dependent. We call this the parameter's source-dependence. Parameters can also vary over any number of image dimensions (a spatially-invariant parameter has dimensionality of 0). We call this the parameter's dimensionality-dependence. Using the parameter dependence classification scheme as a base, the software framework provides tools that allow visualization of filter properties, and where appropriate, interactive user control. A median filter is a simple example of a data dependent (adaptive) filter. We make explicit the components of data analysis and filtering, and use it to show how filter properties can be visualized. A space-variant band-pass filter, used in seismic data processing, shows how user interaction can be incorporated into the framework. Finally, a simple geometric warp shows how geometric (and algorithmic) dependent filters benefit.