Multi-modality image fusion and segmentation play a vital role in autonomous driving and
robotic operation. Early efforts focus on boosting the performance for only one task, eg …

In this paper, we rethink the low-light image enhancement task and propose a physically
explainable and generative diffusion model for low-light image enhancement, termed as Diff …

Deep neural networks have achieved remarkable progress in enhancing low-light images
by improving their brightness and eliminating noise. However, most existing methods …

The correction of exposure-related issues is a pivotal component in enhancing the quality of
images, offering substantial implications for various computer vision tasks. Historically, most …

Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the
limitations of digital imaging in representing varied exposure levels. Despite its …

Ultra-high-definition (UHD) image enhancement is a challenging problem that aims to
effectively and efficiently recover clean UHD images. To maintain efficiency, the …

Photographs taken with less-than-ideal exposure settings often display poor visual quality.
Since the correction procedures vary significantly, it is difficult for a single neural network to …

Existing joint low-light enhancement and deblurring methods learn pixel-wise mappings
from paired synthetic data which results in limited generalization in real-world scenes. While …

In the field of artificial intelligence, combining transformers and convolutional neural
networks (CNNs) to improve performance has become a popular solution for various image …

Image gradients contain crucial information in the images. However, the gradient information
of low-light images is often concealed in darkness and is susceptible to noise contamination …