Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

The recent success of text-to-image synthesis has taken the world by storm and captured the
general public's imagination. From a technical standpoint, it also marked a drastic change in …

Transformer-based methods have shown impressive performance in low-level vision tasks,
such as image super-resolution. However, we find that these networks can only utilize a …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Text-to-image synthesis has recently seen significant progress thanks to large pretrained
language models, large-scale training data, and the introduction of scalable model families …

Recent progress on Transformers and multi-layer perceptron (MLP) models provide new
network architectural designs for computer vision tasks. Although these models proved to be …

The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

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 …

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution. Specifically, by employing our time …