Low-light image enhancement is a notoriously challenging problem. Enhancement of low-
light images is intended to increase contrast, adjust the tone, suppress noise, and produce …

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 …

Single image super-resolution (SISR) is an important research field in computer vision, the
purpose of which is to recover clear, high-resolution (HR) images from low-resolution (LR) …

Measuring the perception of visual content is a long-standing problem in computer vision.
Many mathematical models have been developed to evaluate the look or quality of an …

Abstract No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual
quality of images in accordance with human subjective perception. Unfortunately, existing …

Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed
remarkable progress with deep neural networks. Inspired by the characteristics of the human …

It is widely acknowledged that single image super-resolution (SISR) methods would not
perform well if the assumed degradation model deviates from those in real images. Although …

When capturing and storing images, devices inevitably introduce noise. Reducing this noise
is a critical task called image denoising. Deep learning has become the de facto method for …

Single image super-resolution (SISR) with generative adversarial networks (GAN) has
recently attracted increasing attention due to its potentials to generate rich details. However …

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