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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