  A low-resolution digital surface model (DSM) features distinctive attributes
impacted by noise, sensor limitations and data acquisition conditions, which
failed to be replicated using simple interpolation methods like bicubic. This
causes super-resolution models trained on synthetic data does not perform
effectively on real ones. Training a model on real low and high resolution DSMs
pairs is also a challenge because of the lack of information. On the other
hand, the existence of other imaging modalities of the same scene can be used
to enrich the information needed for large-scale super-resolution. In this
work, we introduce a novel methodology to address the intricacies of real-world
DSM super-resolution, named REAL-GDSR, breaking down this ill-posed problem
into two steps. The first step involves the utilization of a residual local
refinement network. This strategic approach departs from conventional methods
that trained to directly predict height values instead of the differences
(residuals) and utilize large receptive fields in their networks. The second
step introduces a diffusion-based technique that enhances the results on a
global scale, with a primary focus on smoothing and edge preservation. Our
experiments underscore the effectiveness of the proposed method. We conduct a
comprehensive evaluation, comparing it to recent state-of-the-art techniques in
the domain of real-world DSM super-resolution (SR). Our approach consistently
outperforms these existing methods, as evidenced through qualitative and
quantitative assessments.
