# Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery

arXiv: https://arxiv.org/abs/2303.05879 · HTML: https://mlsanity.com/paper/2303.05879/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Handheld Multi-Frame Super-Resolution](https://mlsanity.com/paper/1905.03277/) (2019)
- [The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery](https://mlsanity.com/paper/1812.04098/) (2018)
- [Deep Reparametrization of Multi-Frame Super-Resolution and Denoising](https://mlsanity.com/paper/2108.08286/) (2021)
- [High Dynamic Range and Super-Resolution from Raw Image Bursts](https://mlsanity.com/paper/2207.14671/) (2022)
- [Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites](https://mlsanity.com/paper/2205.02031/) (2022)

## Built on by (most cited first, 5 of 5)

- [Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing](https://mlsanity.com/paper/2404.18924/) (2024)
- [L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery](https://mlsanity.com/paper/2304.06871/) (2023)
- [SuperF: Neural Implicit Fields for Multi-Image Super-Resolution](https://mlsanity.com/paper/2512.09115/) (2025)
- [Keyframe-Centric State-Space Modeling for Burst Image Super-Resolution](https://mlsanity.com/paper/2503.19634/) (2025)
- [Enhanced Self-Supervised Multi-Image Super-Resolution for Camera Array Images](https://mlsanity.com/paper/2604.06816/) (2026)

## Abstract (arXiv)

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.