Text-driven diffusion models have become increasingly popular for various image editing
tasks, including inpainting, stylization, and object replacement. However, it still remains an …

Generating instructional images of human daily actions from an egocentric viewpoint serves
a key step towards efficient skill transfer. In this paper, we introduce a novel problem …

Test-time adaptation (TTA) addresses the unforeseen distribution shifts occurring during test
time. In TTA, both performance and, memory and time consumption serve as crucial …

Natural images captured by mobile devices often suffer from multiple types of degradation,
such as noise, blur, and low light. Traditional image restoration methods require manual …

This paper addresses the limitations of adverse weather image restoration approaches
trained on synthetic data when applied to real-world scenarios. We formulate a semi …

With the emergence of mobile devices, there is a growing demand for an efficient model to
restore any degraded image for better perceptual quality. However, existing models often …

Universal image restoration (UIR) aims to accurately restore images with a variety of
unknown degradation types and levels. Existing methods, including both learning-based …

Image retouching aims to enhance the visual quality of photos. Considering the different
aesthetic preferences of users, the target of retouching is subjective. However, current …

Image restoration involves recovering a high-quality clean image from its degraded version,
which is a fundamental task in computer vision. Recent progress in image restoration has …

Image restoration is a classic low-level problem aimed at recovering high-quality images
from low-quality images with various degradations such as blur, noise, rain, haze, etc …