We present DREAM a novel training framework representing Diffusion Rectification and
Estimation-Adaptive Models requiring minimal code changes (just three lines) yet …

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

Image super-resolution (SR) has attracted increasing attention due to its wide applications.
However, current SR methods generally suffer from over-smoothing and artifacts, and most …

Super-resolution (SR) and image generation are important tasks in computer vision and are
widely adopted in real-world applications. Most existing methods however generate images …

Image super-resolution (SR) is an underdetermined inverse problem, where a large number
of plausible high-resolution images can explain the same downsampled image. Most current …

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

This work introduces" You Only Diffuse Areas"(YODA), a novel method for partial diffusion in
Single-Image Super-Resolution (SISR). The core idea is to utilize diffusion selectively on …

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 …

Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR)
performances in recent years. While most existing work assumes a simple and fixed …