  Performing super-resolution of a depth image using the guidance from an RGB
image is a problem that concerns several fields, such as robotics, medical
imaging, and remote sensing. While deep learning methods have achieved good
results in this problem, recent work highlighted the value of combining modern
methods with more formal frameworks. In this work, we propose a novel approach
which combines guided anisotropic diffusion with a deep convolutional network
and advances the state of the art for guided depth super-resolution. The edge
transferring/enhancing properties of the diffusion are boosted by the
contextual reasoning capabilities of modern networks, and a strict adjustment
step guarantees perfect adherence to the source image. We achieve unprecedented
results in three commonly used benchmarks for guided depth super-resolution.
The performance gain compared to other methods is the largest at larger scales,
such as x32 scaling. Code
(https://github.com/prs-eth/Diffusion-Super-Resolution) for the proposed method
is available to promote reproducibility of our results.
