Image fusion, which refers to extracting and then combining the most meaningful information
from different source images, aims to generate a single image that is more informative and …

Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image
processing and computer vision, which can integrate images with multiple exposure levels …

Multi-exposure image fusion (MEF) targets to integrate multiple shots with different
exposures and generates a single higher dynamic image than each. Existing deep learning …

In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion
framework that uses self-supervised multi-task learning. The framework is based on an …

Pixel-level image fusion, which merges different modal images into an informative image,
has attracted more and more attention. Despite many methods that have been proposed for …

Exposure bracketing is crucial to high dynamic range imaging, but it is prone to halos for
static scenes and ghosting artifacts for dynamic scenes. The recently proposed structural …

Image fusion is an important task for computer vision as a diverse range of applications are
benefiting from the fusion operation. The existing image fusion methods are largely …

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted
increasing interests in recent years. Apart from conventional algorithms, deep learning …

In this paper, we introduce a new approach for high-quality multi-exposure image fusion
(MEF). We show that the fusion weights of an exposure can be encoded into a 1D lookup …

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 …