6 papers
MambaVF: State Space Model for Efficient Video Fusion
Zixiang Zhao, Yukun Cui, Lilun Deng +4
Video fusion is a fundamental technique in various video processing tasks. However, existing video fusion methods heavily rely on optical flow estimation and feature warping, resul…
A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking
Zixiang Zhao, Haowen Bai, Bingxin Ke +5
The real world is dynamic, yet most image fusion methods process static frames independently, ignoring temporal correlations in videos and leading to flickering and temporal incons…
Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +3
Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image il…
Task-driven Image Fusion with Learnable Fusion Loss
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +5
Multi-modal image fusion aggregates information from multiple sensor sources, achieving superior visual quality and perceptual features compared to single-source images, often impr…
Deep Unfolding Multi-modal Image Fusion Network via Attribution Analysis
Haowen Bai, Zixiang Zhao, Jiangshe Zhang +5
Multi-modal image fusion synthesizes information from multiple sources into a single image, facilitating downstream tasks such as semantic segmentation. Current approaches primaril…
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
Haowen Bai, Zixiang Zhao, Jiangshe Zhang +5
Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. Deep learning-based image fusion algorithms fa…