# Spectral Visualization Sharpening

## TL;DR

This paper introduces a perceptually-guided spectral visualization sharpening method that enhances color-mapped visualizations through an adaptable, controllable, and easy-to-use spectral model based on Gaussian pyramid bandpass images.

## Contribution

It presents a novel spectral sharpening technique based on perceptual modeling, offering improved controllability and applicability across various visualization tools.

## Key findings

- Effective sharpening across diverse datasets
- Easy integration into existing visualization tools
- Controllable sharpening with a single viewing distance parameter

## Abstract

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.