  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.
