Search

Your search keyword '"Huang, Jingxue"' showing total 4 results
Start Over Author "Huang, Jingxue"
4 results on '"Huang, Jingxue"'

1. MMA-UNet: A Multi-Modal Asymmetric UNet Architecture for Infrared and Visible Image Fusion

Author

Huang, Jingxue, Li, Xilai, Tan, Tianshu, Li, Xiaosong, and Ye, Tao

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multi-modal image fusion (MMIF) maps useful information from various modalities into the same representation space, thereby producing an informative fused image. However, the existing fusion algorithms tend to symmetrically fuse the multi-modal images, causing the loss of shallow information or bias towards a single modality in certain regions of the fusion results. In this study, we analyzed the spatial distribution differences of information in different modalities and proved that encoding features within the same network is not conducive to achieving simultaneous deep feature space alignment for multi-modal images. To overcome this issue, a Multi-Modal Asymmetric UNet (MMA-UNet) was proposed. We separately trained specialized feature encoders for different modal and implemented a cross-scale fusion strategy to maintain the features from different modalities within the same representation space, ensuring a balanced information fusion process. Furthermore, extensive fusion and downstream task experiments were conducted to demonstrate the efficiency of MMA-UNet in fusing infrared and visible image information, producing visually natural and semantically rich fusion results. Its performance surpasses that of the state-of-the-art comparison fusion methods.

Published
2024

3. Multimodal Medical Image Fusion Based on Multichannel Aggregated Network

Author

Huang, Jingxue, Li, Xiaosong, Tan, Haishu, Cheng, Xiaoqi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lu, Huchuan, editor, Ouyang, Wanli, editor, Huang, Hui, editor, Lu, Jiwen, editor, Liu, Risheng, editor, Dong, Jing, editor, and Xu, Min, editor

Published
2023
Full Text
View/download PDF

4. Generative Adversarial Network for Trimodal Medical Image Fusion Using Primitive Relationship Reasoning.

Author

Huang J, Li X, Tan H, and Cheng X

Subjects
Humans, Image Interpretation, Computer-Assisted methods, Neural Networks, Computer, Brain diagnostic imaging, Magnetic Resonance Imaging methods, Brain Neoplasms diagnostic imaging, Glioma diagnostic imaging, Image Processing, Computer-Assisted methods, Algorithms
Abstract

Medical image fusion has become a hot biomedical image processing technology in recent years. The technology coalesces useful information from different modal medical images onto an informative single fused image to provide reasonable and effective medical assistance. Currently, research has mainly focused on dual-modal medical image fusion, and little attention has been paid on trimodal medical image fusion, which has greater application requirements and clinical significance. For this, the study proposes an end-to-end generative adversarial network for trimodal medical image fusion. Utilizing a multi-scale squeeze and excitation reasoning attention network, the proposed method generates an energy map for each source image, facilitating efficient trimodal medical image fusion under the guidance of an energy ratio fusion strategy. To obtain the global semantic information, we introduced squeeze and excitation reasoning attention blocks and enhanced the global feature by primitive relationship reasoning. Through extensive fusion experiments, we demonstrate that our method yields superior visual results and objective evaluation metric scores compared to state-of-the-art fusion methods. Furthermore, the proposed method also obtained the best accuracy in the glioma segmentation experiment.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results