Diffusion models (DMs) have disrupted the image super-resolution (SR) field and further
closed the gap between image quality and human perceptual preferences. They are easy to …

Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …

Abstract We introduce SUPIR (Scaling-UP Image Restoration) a groundbreaking image
restoration method that harnesses generative prior and the power of model scaling up …

Given a single image of a 3D object this paper proposes a novel method (named
ConsistNet) that can generate multiple images of the same object as if they are captured …

Denoising diffusion models have emerged as a powerful tool for various image generation
and editing tasks, facilitating the synthesis of visual content in an unconditional or input …

Generative models have recently exhibited exceptional capabilities in text-to-image
generation but still struggle to generate image sequences coherently. In this work we focus …

Recently diffusion models (DM) have been applied in magnetic resonance imaging (MRI)
super-resolution (SR) reconstruction exhibiting impressive performance especially with …

Eliminating image blur produced by various kinds of motion has been a challenging
problem. Dominant approaches rely heavily on model capacity to remove blurring by …

Abstract Recovering ghost-free High Dynamic Range (HDR) images from multiple Low
Dynamic Range (LDR) images becomes challenging when the LDR images exhibit …

Though diffusion models have been successfully applied to various image restoration (IR)
tasks their performance is sensitive to the choice of training datasets. Typically diffusion …