Papers › Deep Burst Super-Resolution
Deep Burst Super-Resolution
Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte
While single-image super-resolution (SISR) has attracted substantial interest in recent years, the proposed approaches are limited to learning image priors in order to add high frequency details. In contrast, multi-frame super-resolution (MFSR) offers the possibility of reconstructing rich details by combining signal information from multiple shifted images. This key advantage, along with the increasing popularity of burst photography, have made MFSR an important problem for real-world applications. We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. This is achieved by explicitly aligning deep embeddings of the input frames using pixel-wise optical flow. The information from all frames are then adaptively merged using an attention-based fusion module. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset, consisting of smartphone bursts and high-resolution DSLR ground-truth. We perform comprehensive experimental analysis, demonstrating the effectiveness of the proposed architecture.
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Code
Syntology Ran 6 of 7 code samples harvested from 3 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · fixture could not drive it; 4 ran with no contract checked.
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Code Syntology ran Syntology
7 samples harvested; 6 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Burst Image Super-Resolution | BurstSR | DBSR | LPIPS | 0.029 | #9 of 9 | Archive leaderboard | report |
| Burst Image Super-Resolution | BurstSR | DBSR | PSNR | 47.70 | #9 of 9 | Archive leaderboard | report |
| Burst Image Super-Resolution | BurstSR | DBSR | SSIM | 0.984 | #9 of 9 | Archive leaderboard | report |
| Burst Image Super-Resolution | SyntheticBurst | DBSR | LPIPS | 0.081 | #8 of 8 | Archive leaderboard | report |
| Burst Image Super-Resolution | SyntheticBurst | DBSR | PSNR | 39.17 | #8 of 8 | Archive leaderboard | report |
| Burst Image Super-Resolution | SyntheticBurst | DBSR | SSIM | 0.946 | #8 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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