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 …

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 …

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-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 …

We aim at advancing blind image quality assessment (BIQA), which predicts the human
perception of image quality without any reference information. We develop a general and …

We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-
LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel …

This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge focused on
User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based …

With the rapid advancements of the text-to-image generative model, AI-generated images
(AGIs) have been widely applied to entertainment, education, social media, etc. However …

The rapid increase in user-generated-content (UGC) videos calls for the development of
effective video quality assessment (VQA) algorithms. However, the objective of the UGC …

Text-to-image generative models often reflect the biases of the training data, leading to
unequal representations of underrepresented groups. This study investigates inclusive text …