Diffusion models have gained significant popularity for image-to-image translation tasks. Previous efforts applying diffusion models to image super-resolution 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, we adopt existing image super-resolution methods and finetune them to provide conditional images from given low-resolution images, which can help to achieve better high-resolution results than just taking low-resolution images as conditional images. Then we adapt the diffusion model to perform super-resolution through a deterministic iterative denoising process, which helps to strongly decline the inference time. 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.

At present, most mainstream algorithms for single image super-resolution (SISR) assume the image degradation process as an ideal degradation process (e.g. bicubic downscaling), which violates the actual degeneration conditions. In real-world image capturing, objects often move in a dynamic environment, and camera shake also often occurs, which results in serious blurs. Our work focuses on the task of image super-resolution with heavy motion blur, for which we adopt a network with two branches: one branch for image deblurring and the other one for super-resolution. Since the features obtained by the deblurring are rich in details, we apply their features as supplementary information to the super-resolution branch. Based on the adopted dual-branch framework, our major technical novelties lie in two novel modules: Multi-Scale Feature Fusion (MSFF1) module which fuses features of different scale from the deblurring branch to get local and global information, and Multi-Stage Feature Fusion (MSFF2) module which further filters useful information with attention. We evaluate the proposed method under various blur scenarios on the benchmark datasets, demonstrating competitive performance against existing methods.