Recent text-to-image generative models can generate high-fidelity images from text inputs,
but the quality of these generated images cannot be accurately evaluated by existing …

While it is nearly effortless for humans to quickly assess the perceptual similarity between
two images, the underlying processes are thought to be quite complex. Despite this, the …

Automatically learned quality assessment for images has recently become a hot topic due to
its usefulness in a wide variety of applications, such as evaluating image capture pipelines …

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors
including composition, color, style, and high-level semantics. Existing image aesthetic …

The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift
in computer vision from specialized models to general-purpose foundation models …

Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved
problem of great consequence to the social and streaming media industries that impacts …

Nighttime image dehazing is a challenging task due to the presence of multiple types of
adverse degrading effects including glow, haze, blur, noise, color distortion, and so on …

Low-light image (LLI) enhancement is an important image processing task that aims at
improving the illumination of images taken under low-light conditions. Recently, a …

For all the ways convolutional neural nets have revolutionized computer vision in recent
years, one important aspect has received surprisingly little attention: the effect of image size …

We study the problem of photo cropping, which aims to find a cropping window of an input
image to preserve as much as possible its important parts while being aesthetically …