Toward task-driven satellite image super-resolution
This work addresses the need for more effective super-resolution in satellite image analysis, but it is incremental as it focuses on methodological assessment rather than new algorithms.
The paper tackles the problem of unclear value of super-resolution details for automated image analysis by proposing a task-driven approach to assess and train super-resolution models, with initial research establishing a foundation for selecting appropriate computer vision tasks.
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.