Efficient Burst Super-Resolution with One-step Diffusion

• URL: http://arxiv.org/abs/2507.13607v1
• Date: Fri, 18 Jul 2025 02:21:29 GMT
• Title: Efficient Burst Super-Resolution with One-step Diffusion
• Authors: Kento Kawai, Takeru Oba, Kyotaro Tokoro, Kazutoshi Akita, Norimichi Ukita,
• Abstract summary: Prior burst Low-Resolution (LR) images produce a blurry Super Resolution (SR) image.<n>Since such blurry images are perceptually degraded, we aim to reconstruct sharp and high-fidelity SR images by a diffusion model.<n>Our method can reduce the runtime to 1.6 % of its baseline while maintaining the SR quality measured based on image distortion and perceptual quality.
• Score: 12.398029922896374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While burst Low-Resolution (LR) images are useful for improving their Super Resolution (SR) image compared to a single LR image, prior burst SR methods are trained in a deterministic manner, which produces a blurry SR image. Since such blurry images are perceptually degraded, we aim to reconstruct sharp and high-fidelity SR images by a diffusion model. Our method improves the efficiency of the diffusion model with a stochastic sampler with a high-order ODE as well as one-step diffusion using knowledge distillation. Our experimental results demonstrate that our method can reduce the runtime to 1.6 % of its baseline while maintaining the SR quality measured based on image distortion and perceptual quality.

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