Diffusion models significantly improve the quality of super-resolved images with their
impressive content generation capabilities. However, the huge computational costs limit the …

While super-resolution (SR) methods based on diffusion models exhibit promising results
their practical application is hindered by the substantial number of required inference steps …

Diffusion models have gained significant popularity for image-to-image translation tasks.
Previous efforts applying diffusion models to image super-resolution have demonstrated that …

Diffusion-based image super-resolution (SR) methods are mainly limited by the low
inference speed due to the requirements of hundreds or even thousands of sampling steps …

Image super-resolution is a fundamentally ill-posed problem because multiple valid high-
resolution images exist for one low-resolution image. Super-resolution methods based on …

Denoising diffusion probabilistic models (DDPMs) have achieved impressive performance
on various image generation tasks, including image super-resolution. By learning to reverse …

We propose a novel Conditional Guided Diffusion Probabilistic Model (CG-DPM) for image
super-resolution. CG-DPM adopts diffusion models, which have strong abilities to generate …

In this paper, we introduce YONOS-SR, a novel stable diffusion-based approach for image
super-resolution that yields state-of-the-art results using only a single DDIM step. We …

Single image super-resolution (SISR) refers to the reconstruction from the corresponding
low-resolution (LR) image input to a high-resolution (HR) image. However, since a single …

Diffusion-based super-resolution (SR) models have recently garnered significant attention
due to their potent restoration capabilities. But conventional diffusion models perform noise …