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.

翻译：提高行星地形模型的分辨率有助於更深入地理解地表過程與地貌特徵；然而，現有的解析超解析度方法成本高昂，難以大規模應用。生成式模型提供了學習數據中複雜關係的工具，並可藉助硬體加速器與平行化技術實現規模化應用。我們提出一種基於擴散的薛定諤橋（Schrödinger Bridge, SB）生成建模方法，用於月球地形超解析度，該方法將低解析度地形分布與高解析度地形分布相連接，並融入具物理約束的光學影像。本方法受現有陰影恢復形狀（Shape-from-Shading）技術啟發，該技術利用目標解析度的光學影像改進先驗低解析度地形。我們在一個新穎的渲染月球地形數據集上訓練薛定諤橋模型，該數據集模擬了月球勘測軌道飛行器窄角相機（Lunar Reconnaissance Orbiter Narrow Angle Camera）所獲取的光學影像。最終成果是一種靈活的地形超解析度方法，可提供重建過程中像素級別的不確定性。