Laser & Optoelectronics Progress, Volume. 62, Issue 14, 1439003(2025)
Infrared-Visible Image Fusion Network Based on Dual-Branch Feature Decomposition
[8] Chan G, Zhang P, Dong H et al. Scribble-supervised semantic segmentation with prototype-based feature augmentation[C](2024).
[16] Zhang H, Han X N, Han H L et al. Two-scale image fusion of visible and infrared images based on guided filtering decomposition[J]. Infrared Technology, 45, 1216-1222(2023).
[36] Yang Y, Ren Z N, Li B C. Infrared and visible image fusion with convolutional neural network and transformer[J]. Laser & Optoelectronics Progress, 60, 1610013(2023).
[40] Yin H T, Zhou C S. Cross-fusion Transformer-based infrared and visible image fusion method[J]. Laser & Optoelectronics Progress, 62, 0637002(2025).
[48] Wang Z S, Chen Y L, Shao W Y et al. SwinFuse: a residual swin transformer fusion network for infrared and visible images[J]. IEEE Transactions on Instrumentation and Measurement, 71, 5016412(2006).
[49] Yuan L, Hou Q B, Jiang Z H et al. VOLO: vision outlooker for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 6575-6586(2023).
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Xundong Gao, Hui Chen, Yaning Yao, Chengcheng Zhang. Infrared-Visible Image Fusion Network Based on Dual-Branch Feature Decomposition[J]. Laser & Optoelectronics Progress, 2025, 62(14): 1439003
Category: AI for Optics
Received: Dec. 23, 2024
Accepted: Mar. 2, 2025
Published Online: Jul. 16, 2025
The Author Email: Xundong Gao (xundonggao@guet.edu.cn), Hui Chen (Chenhui02@guet.edu.cn)
CSTR:32186.14.LOP242481