Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinement process comes with the drawback of low inference speed, which strongly limits its applications. To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution). Specifically, our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our study also highlights the effectiveness of using a pre-trained SR model to provide the conditional image of the given low-resolution (LR) image to achieve superior high-resolution results. We demonstrate that our method surpasses previous attempts in qualitative and quantitative results through extensive experiments conducted on benchmark datasets such as Set5, Set14, Urban100, BSD100, and Manga109. Moreover, our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.

翻译：扩散模型在图像到图像翻译领域获得了广泛关注。先前将扩散模型应用于图像超分辨率（SR）的研究表明，通过使用在不同噪声水平下训练的去噪U-Net架构对纯高斯噪声进行迭代细化，可以从低分辨率输入生成令人满意的高分辨率图像。然而，这种迭代细化过程存在推理速度慢的缺点，严重限制了其应用。为了加速推理并进一步提升性能，本研究重新审视了图像超分辨率中的扩散模型，并提出了一种简单但重要的基于扩散模型的超分辨率方法，称为ACDMSR（加速条件扩散模型的图像超分辨率）。具体而言，我们的方法通过确定性迭代去噪过程，将标准扩散模型适配到超分辨率任务中。本研究还强调了使用预训练SR模型为给定的低分辨率（LR）图像提供条件图像的有效性，以实现更优的高分辨率结果。通过在Set5、Set14、Urban100、BSD100和Manga109等基准数据集上进行的大量实验证明，我们的方法在定性和定量结果上均超越了先前的尝试。此外，我们的方法为低分辨率图像生成了更具视觉真实性的对应图像，突显了其在实际场景中的有效性。