  In this paper, we propose a perceptually-guided visualization sharpening
technique. We analyze the spectral behavior of an established comprehensive
perceptual model to arrive at our approximated model based on an adapted
weighting of the bandpass images from a Gaussian pyramid. The main benefit of
this approximated model is its controllability and predictability for
sharpening color-mapped visualizations. Our method can be integrated into any
visualization tool as it adopts generic image-based post-processing, and it is
intuitive and easy to use as viewing distance is the only parameter. Using
highly diverse datasets, we show the usefulness of our method across a wide
range of typical visualizations.
