Toward task-driven satellite image super-resolution
Journal:
arXiv
Published Date:
Mar 19, 2025
Abstract
Super-resolution is aimed at reconstructing high-resolution images from
low-resolution observations. State-of-the-art approaches underpinned with deep
learning allow for obtaining outstanding results, generating images of high
perceptual quality. However, it often remains unclear whether the reconstructed
details are close to the actual ground-truth information and whether they
constitute a more valuable source for image analysis algorithms. In the
reported work, we address the latter problem, and we present our efforts toward
learning super-resolution algorithms in a task-driven way to make them suitable
for generating high-resolution images that can be exploited for automated image
analysis. In the reported initial research, we propose a methodological
approach for assessing the existing models that perform computer vision tasks
in terms of whether they can be used for evaluating super-resolution
reconstruction algorithms, as well as training them in a task-driven way. We
support our analysis with experimental study and we expect it to establish a
solid foundation for selecting appropriate computer vision tasks that will
advance the capabilities of real-world super-resolution.
