  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.
