H
Han Xu
Researcher at Wuhan University
Publications - 25
Citations - 2197
Han Xu is an academic researcher from Wuhan University. The author has contributed to research in topics: Image fusion & Computer science. The author has an hindex of 11, co-authored 16 publications receiving 478 citations.
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DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion
TL;DR: A new end-to-end model, termed as dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions, which establishes an adversarial game between a generator and two discriminators.
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U2Fusion: A Unified Unsupervised Image Fusion Network.
TL;DR: Qualitative and quantitative experimental results on three typical image fusion tasks validate the effectiveness and universality of U2Fusion, a unified model that is applicable to multiple fusion tasks.
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Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity
TL;DR: This paper unify the image fusion problem into the texture and intensity proportional maintenance problem of the source images, and defines a uniform form of loss function based on these two kinds of information, which can adapt to different fusion tasks.
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FusionDN: A Unified Densely Connected Network for Image Fusion.
TL;DR: A new unsupervised and unified densely connected network for different types of image fusion tasks, termed as FusionDN, which obtains a single model applicable to multiple fusion tasks by applying elastic weight consolidation to avoid forgetting what has been learned from previous tasks when training multiple tasks sequentially.
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Image fusion meets deep learning: A survey and perspective
TL;DR: In this paper, a comprehensive review and analysis of latest deep learning methods in different image fusion scenarios is provided, and the evaluation for some representative methods in specific fusion tasks are performed qualitatively and quantitatively.