Infrared and visible image fusion targets to provide an informative image by combining
complementary information from different sensors. Existing learning-based fusion …

Multi-modal semantic segmentation significantly enhances AI agents' perception and scene
understanding, especially under adverse conditions like low-light or overexposed …

Image fusion aims to combine information from different source images to create a
comprehensively representative image. Existing fusion methods are typically helpless in …

Multi-modality image fusion is a technique that combines information from different sensors
or modalities enabling the fused image to retain complementary features from each modality …

Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially
for enhancing visual quality and/or extracting aggregated features for perception. However …

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 …

Pan-sharpening seeks to generate a high-resolution multispectral (HRMS) image by
merging the high-resolution panchromatic (PAN) image and its low-resolution multispectral …

Image super-resolution (SR) is a critical technique for enhancing image quality, playing a
vital role in image enhancement. While recent advancements, notably transformer-based …

Recovering a clear image from a single hazy image is an open inverse problem. Although
significant research progress has been made most existing methods ignore the effect that …

Pansharpening is a technique that combines a high-resolution panchromatic image
(HRPAN) and a low-resolution multi-spectral image (LRMS) to generate a high-resolution …