用一步扩散模型提升突发低清图像超分辨率效率与清晰度
Efficient Burst Super-Resolution with One-step Diffusion
- 采用高阶常微分方程随机采样与知识蒸馏实现一步扩散
- 运行时间仅为基线1.6%，图像质量保持不变
- 适合需要实时高清图像重建的移动端应用
尽管突发低分辨率（LR）图像相比单张LR图像能提升超分辨率（SR）效果，但以往方法采用确定性训练，导致生成图像模糊。为重建清晰、高保真的SR图像，本文引入扩散模型。通过高阶常微分方程的随机采样和基于知识蒸馏的一步扩散，显著提升扩散模型效率。实验表明，该方法将运行时间降至基线的1.6%，同时在图像失真和感知质量上均保持原有水平。
原文摘要 · Abstract (English)
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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