Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spectral Visualization Sharpening   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.

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The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom