Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery
This work addresses the need for high-resolution, noise-free satellite imagery for remote sensing applications, but it is incremental as it adapts an existing method to a new domain.
The paper tackled the problem of enhancing satellite image resolution by adapting a kernel regression technique from smartphone burst super-resolution to satellites, achieving a zoom factor of 2 with denoising and detail recovery without hallucination.
Image resolution is an important criterion for many applications based on satellite imagery. In this work, we adapt a state-of-the-art kernel regression technique for smartphone camera burst super-resolution to satellites. This technique leverages the local structure of the image to optimally steer the fusion kernels, limiting blur in the final high-resolution prediction, denoising the image, and recovering details up to a zoom factor of 2. We extend this approach to the multi-exposure case to predict from a sequence of multi-exposure low-resolution frames a high-resolution and noise-free one. Experiments on both single and multi-exposure scenarios show the merits of the approach. Since the fusion is learning-free, the proposed method is ensured to not hallucinate details, which is crucial for many remote sensing applications.