用深度学习提升月球地形分辨率，支持像素级不确定性估计。
Improving Lunar Topography with Deep Learning Schrödinger Bridges
- 基于扩散模型的薛定谔桥方法，连接高低分辨率地形分布
- 在模拟月球影像数据上训练，实现高精度地形超分辨率重建
- 可输出重建置信度，适合地质与测绘研究者使用
提高行星地形模型的分辨率有助于更深入理解地表过程与地貌特征；然而，现有的解析型超分辨率方法成本高昂且难以大规模应用。生成模型能够学习数据间的复杂关系，并借助硬件加速器和并行计算实现规模化应用。本文提出一种基于扩散的薛定谔桥（Schrödinger Bridge, SB）生成建模方法，用于月球地形超分辨率，将低分辨率地形分布与高分辨率地形分布相连接，同时融入物理约束的光学影像。该方法受现有形态-阴影（Shape-from-Shading）方法启发，通过目标分辨率的光学图像优化先验低分辨率地形。我们在一个新构建的渲染月球地形数据集上训练SB模型，该数据集模拟了月球勘测轨道器窄角相机（LRO NAC）的光学影像。结果表明，该方法具备灵活性，能提供像素级的重建不确定性，适用于大规模地形精细化分析。
原文摘要 · Abstract (English)
Increasing the resolution of planetary topography models can enable a better understanding of surface processes and geomorphology; however, existing analytical super-resolution methods are expensive and difficult to apply at large scales. Generative models provide the tools to learn complex relationships within data and can be applied at scale due to hardware accelerators and parallelization. We present a diffusion-based Schrödinger Bridge (SB) generative modeling approach for lunar topography super-resolution, connecting the distribution of low-resolution topography to that of high-resolution topography, incorporating physically-constraining optical imagery. Our approach is inspired by existing Shape-from-Shading methods, which improve a priori low-resolution topography by using optical images at the target resolution. We train SBs on a novel dataset of rendered lunar topography, emulating optical imagery from the Lunar Reconnaissance Orbiter Narrow Angle Camera. The result is a flexible approach for topography super-resolution which can provide pixel-level uncertainties in the reconstruction.
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