Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

Visible and infrared image fusion (VIF) has attracted a lot of interest in recent years due to its
application in many tasks, such as object detection, object tracking, scene segmentation …

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

Image fusion aims to integrate complementary information in source images to synthesize a
fused image comprehensively characterizing the imaging scene. However, existing image …

As a vital image enhancement technology, infrared and visible image fusion aims to
generate high-quality fused images with salient targets and abundant texture in extreme …

We introduce a novel method using a new generative model that automatically learns
effective representations of the target and background appearance to detect, segment and …

Deep learning based fusion methods have been achieving promising performance in image
fusion tasks. This is attributed to the network architecture that plays a very important role in …

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 …

Image fusion aims to integrate complementary characteristics of source images into a single
fused image that better serves human visual observation and machine vision perception …

This research focuses on the discovery and localization of hidden objects in the wild and
serves unmanned systems. Through empirical analysis, infrared and visible image fusion …