Space-variant filtering is generally expensive and difficult to implement in a generic manner. As a result, conventional image filtering is largely space-invariant. Much imagery, such as sensed or modeled data that is geometrically distorted, requires space-variant filtering if data sampling integrity is to be preserved. Space-variant filtering under interactive control can better enable the expertise of an application specialist because filter kernel characteristics, and the result of applying the filters, can be visualized simultaneously as parameters are adjusted. This paper shows how space-variant filters can be generated, modified, and applied to real filtering problems interactively using visualization of filter kernel images and the effects of their application. Massively parallel processing is exploited to provide scalable realizations of the filtering, in which space-variant filters of varying type and bandwidth are embedded within parallel tool-kits. Control of filter characteristics is achieved using image masks derived from interaction, from data properties, from modeling parameters, and from data format information. Application examples show space-variant filtering requirements for surface modeling to avoid smoothing regions of high spatial frequency but which allow smoothness in regions of low spatial frequency, together with geometrically and parametrically derived filtering.