BurstGP: Enhancing Raw Burst Image Super Resolution with Generative Priors
Dong Huo, Tristan Aumentado-Armstrong, Samrudhdhi B. Rangrej, Maitreya Suin, Angela Ning Ye, Zhiming Hu, Amanpreet Walia, Amirhossein Kazerouni, Konstantinos G. Derpanis, Iqbal Mohomed, Alex Levinshtein

TL;DR
BurstGP introduces a diffusion-based method leveraging generative priors and a novel conditioning mechanism to significantly improve burst image super-resolution, especially in recovering textures and details.
Contribution
The paper presents BurstGP, a multiframe-aware diffusion model that enhances BISR by integrating generative priors, a degradation-aware conditioning mechanism, and a sRGB-to-lRGB inverter.
Findings
Outperforms state-of-the-art methods quantitatively and qualitatively.
Excels at recovering richer textures and finer structural details.
Achieves higher perceptual quality metrics like MUSIQ and LPIPS.
Abstract
Burst image super resolution (BISR) aims to construct a single high-resolution (HR) image by aggregating information from multiple low-resolution (LR) frames, relying on temporal redundancy and spatial coherence across the burst. While conventional methods achieve impressive results, they often struggle with complex textures and oversmoothing. Diffusion models, particularly those pretrained on high-quality data, have shown remarkable capability in generating realistic details for image and video super-resolution. However, their potential remains largely under-explored in BISR, where existing approaches typically rely on task-specific diffusion models trained from scratch and operate on single-frame reconstructions. In this work, we propose BurstGP, a novel diffusion-based solution for BISR, which leverages generative priors of recent foundation models to overcome these issues. In…
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