光谱可视化锐化

Liang Zhou, Rudolf Netzel, D. Weiskopf, Chris R. Johnson
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引用次数: 2

摘要

在本文中,我们提出了一种感知引导的可视化锐化技术。我们分析了一个已建立的综合感知模型的光谱行为,以基于高斯金字塔的带通图像的自适应加权得出我们的近似模型。这种近似模型的主要好处是它的可控性和可预测性锐化颜色映射的可视化。该方法采用通用的基于图像的后处理,可集成到任何可视化工具中,且仅以观察距离为参数,直观易用。使用高度多样化的数据集,我们展示了我们的方法在广泛的典型可视化中的有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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