Precise movement during micromanipulation becomes difficult in submillimeter workspaces, largely due to the destabilizing influence of tremor. Robotic aid combined with filtering techniques that suppress tremor frequency bands increases performance; however, if knowledge of the operator's goals is available, virtual fixtures have been shown to greatly improve micromanipulator precision. In this paper, we derive a control law for position-based virtual fixtures within the framework of an active handheld micromanipulator, where the fixtures are generated in real-time from microscope video. Additionally, we develop motion scaling behavior centered on virtual fixtures as a simple and direct extension to our formulation. We demonstrate that hard and soft (motion-scaled) virtual fixtures outperform state-of-the-art tremor cancellation performance on a set of artificial but medically relevant tasks: holding, move-and-hold, curve tracing, and volume restriction.