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 …

We present DiffBIR, a general restoration pipeline that could handle different blind image
restoration tasks in a unified framework. DiffBIR decouples blind image restoration problem …

Diffusion models have achieved promising results in image restoration tasks, yet suffer from
time-consuming, excessive computational resource consumption, and unstable restoration …

Abstract We present Prompt Diffusion, a framework for enabling in-context learning in
diffusion-based generative models. Given a pair of task-specific example images, such as …

While deep learning-based methods for blind face restoration have achieved
unprecedented success, they still suffer from two major limitations. First, most of them …

Current image steganography techniques are mainly focused on cover-based methods,
which commonly have the risk of leaking secret images and poor robustness against …

We introduce beta diffusion, a novel generative modeling method that integrates demasking
and denoising to generate data within bounded ranges. Using scaled and shifted beta …

Abstract We present MoE-DiffIR, an innovative universal compressed image restoration
(CIR) method with task-customized diffusion priors. This intends to handle two pivotal …

The generative priors of pre-trained latent diffusion models (DMs) have demonstrated great
potential to enhance the visual quality of image super-resolution (SR) results. However, the …