Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery
- URL: http://arxiv.org/abs/2303.05879v1
- Date: Fri, 10 Mar 2023 12:13:31 GMT
- Title: Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery
- Authors: Jamy Lafenetre, Ngoc Long Nguyen, Gabriele Facciolo, Thomas Eboli
- Abstract summary: We adapt a state-of-the-art kernel regression technique for smartphone camera burst super-resolution to satellites.
We leverage the local structure of the image to optimally steer the fusion kernels, limiting blur in the final high-resolution prediction.
We extend this approach to predict from a sequence of multi-exposure low-resolution frames a high-resolution and noise-free one.
- Score: 7.9716992946722804
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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