  Digital Elevation Model (DEM) plays a fundamental role in remote sensing and
photogrammetry. Enhancing the quality of DEM is crucial for various
applications. Although multiple types of defects may appear simultaneously in
the same DEM, they are commonly addressed separately. Most existing approaches
only aim to fill the DEM voids, or apply super-resolution to the intact DEM.
This paper introduces a unified generative model that simultaneously addresses
voids and low-resolution problems, rather than taking two separate measures.
The proposed approach presents the DEM Stochastic Differential Equation
(DEM-SDE) for unified DEM quality enhancement. The DEM degradation of
downsampling and random voids adding is modeled as the SDE forwarding, and the
restoration is achieved by simulating the corresponding revert process.
Conditioned on the terrain feature, and adopting efficient submodules with
lightweight channel attention, DEM-SDE simultaneously enhances the DEM quality
with an efficient process for training. The experiments show that DEM-SDE
method achieves highly competitive performance in simultaneous super-resolution
and void filling compared to the state-of-the-art work. DEM-SDE also manifests
robustness for larger DEM patches.
