While burst LR images are useful for improving the SR image quality compared with a single LR image, prior SR networks accepting the burst LR images are trained in a deterministic manner, which is known to produce a blurry SR image. In addition, it is difficult to perfectly align the burst LR images, making the SR image more blurry. Since such blurry images are perceptually degraded, we aim to reconstruct the sharp high-fidelity boundaries. Such high-fidelity images can be reconstructed by diffusion models. However, prior SR methods using the diffusion model are not properly optimized for the burst SR task. Specifically, the reverse process starting from a random sample is not optimized for image enhancement and restoration methods, including burst SR. In our proposed method, on the other hand, burst LR features are used to reconstruct the initial burst SR image that is fed into an intermediate step in the diffusion model. This reverse process from the intermediate step 1) skips diffusion steps for reconstructing the global structure of the image and 2) focuses on steps for refining detailed textures. Our experimental results demonstrate that our method can improve the scores of the perceptual quality metrics. Code: https://github.com/placerkyo/BSRD

翻译：尽管与单张低分辨率图像相比，多张突发低分辨率图像有助于提升超分辨率图像质量，但现有接受突发低分辨率图像作为输入的超分辨率网络通常以确定性方式训练，这会导致生成的超分辨率图像模糊。此外，突发低分辨率图像的完美对齐十分困难，进一步加剧了超分辨率图像的模糊程度。由于此类模糊图像在感知上存在质量退化，我们旨在重建清晰且高保真的边界。扩散模型能够重建此类高保真图像，但现有基于扩散模型的超分辨率方法并未针对突发超分辨率任务进行合理优化。具体而言，从随机采样出发的逆扩散过程并未针对包括突发超分辨率在内的图像增强与复原方法进行优化。而我们所提出的方法利用突发低分辨率特征重建初始的超分辨率图像，并将其输入扩散模型中的中间步骤。该从中间步骤开始的逆扩散过程能够：1) 跳过用于重建图像全局结构的扩散步骤，2) 专注于细化细节纹理的步骤。实验结果表明，我们的方法能够提升感知质量指标的评分。代码地址：https://github.com/placerkyo/BSRD