论文标题
部分可观测时空混沌系统的无模型预测
Deep Learning based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering
论文作者
论文摘要
现代显示系统需要高质量的渲染。但是，以较高分辨率渲染需要大量的数据样本，并且计算昂贵。基于深度学习的图像和视频超分辨率技术的最新进展激发了我们调查此类网络，以确保以较低分辨率提高到更高分辨率的框架的高保真升级。尽管我们的工作着重于通过直接体积渲染执行的医疗量可视化的超分辨率，但它也适用于使用其他渲染技术可视化。我们提出了一种基于学习的技术，我们提出的系统使用颜色信息以及从我们的音量渲染器收集的其他补充功能，以了解对高分辨率渲染到高分辨率空间的有效升级。此外，为了提高时间稳定性，我们还实施了用于在体积渲染中积累历史样本的时间再现技术。
Modern-day display systems demand high-quality rendering. However, rendering at higher resolution requires a large number of data samples and is computationally expensive. Recent advances in deep learning-based image and video super-resolution techniques motivate us to investigate such networks for high-fidelity upscaling of frames rendered at a lower resolution to a higher resolution. While our work focuses on super-resolution of medical volume visualization performed with direct volume rendering, it is also applicable for volume visualization with other rendering techniques. We propose a learning-based technique where our proposed system uses color information along with other supplementary features gathered from our volume renderer to learn efficient upscaling of a low-resolution rendering to a higher-resolution space. Furthermore, to improve temporal stability, we also implement the temporal reprojection technique for accumulating history samples in volumetric rendering.
