Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery
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.
Cite
Text
Lafenetre et al. "Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023. doi:10.1109/CVPRW59228.2023.00199Markdown
[Lafenetre et al. "Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2023.](https://mlanthology.org/cvprw/2023/lafenetre2023cvprw-handheld/) doi:10.1109/CVPRW59228.2023.00199BibTeX
@inproceedings{lafenetre2023cvprw-handheld,
title = {{Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery}},
author = {Lafenetre, Jamy and Nguyen, Ngoc Long and Facciolo, Gabriele and Eboli, Thomas},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2023},
pages = {2056-2064},
doi = {10.1109/CVPRW59228.2023.00199},
url = {https://mlanthology.org/cvprw/2023/lafenetre2023cvprw-handheld/}
}