Infrared-Visible Image Fusion via Heterogeneous Multi-Level Distillation

ZHANG Qi; SONG Hong; LI Jin-fu; MA Shi-han; LIN Yu-cong; YANG Jian

doi:10.12263/DZXB.20250764

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Infrared-Visible Image Fusion via Heterogeneous Multi-Level Distillation

Special Issue\: Recipients of CIE Science and Technology Awards | 更新时间：2026-04-24

- Infrared-Visible Image Fusion via Heterogeneous Multi-Level Distillation
- ACTA ELECTRONICA SINICA Vol. 53, Issue 12, Pages: 4250-4266(2025)
- 作者机构：
  
  1.北京理工大学计算机学院，北京 100081
  2.北京理工大学光电学院，北京 100081
- 作者简介：
- 基金信息：
  
  Beijing Natural Science Foundation(L242024);National Natural Science Foundation of China(U22A2052)
- DOI：10.12263/DZXB.20250764
  CLC： TP391.4;TH701
- Received：04 September 2025，
  
  Accepted：08 December 2025，
  
  Published：25 December 2025
- 稿件说明：
移动端阅览
张棋, 宋红, 李金夫, 等. 异构模型多层次蒸馏的红外-可见光图像融合[J]. 电子学报, 2025, 53(12): 4250-4266.

ZHANG Qi, SONG Hong, LI Jin-fu, et al. Infrared-Visible Image Fusion via Heterogeneous Multi-Level Distillation[J]. Acta Electronica Sinica, 2025, 53(12): 4250-4266.
张棋, 宋红, 李金夫, 等. 异构模型多层次蒸馏的红外-可见光图像融合[J]. 电子学报, 2025, 53(12): 4250-4266. DOI：10.12263/DZXB.20250764

ZHANG Qi, SONG Hong, LI Jin-fu, et al. Infrared-Visible Image Fusion via Heterogeneous Multi-Level Distillation[J]. Acta Electronica Sinica, 2025, 53(12): 4250-4266. DOI：10.12263/DZXB.20250764

摘要

知识蒸馏可将复杂教师网络的表征能力迁移至轻量学生网络，有效提升模型性能与部署效率.然而，现有基于知识蒸馏的多模态图像融合方法常忽视师生网络的特征表示、模态偏好异构性及多模态图像的固有差异，导致知识传递低效、语义对齐不足及融合性能退化.针对上述问题，本文提出基于异构模型多层次知识蒸馏的红外与可见光图像融合方法，创新性设计跨层级知识传递机制，在特征层通过注意力引导红外显著性目标与可见光纹理的精准迁移；在关系层通过相似性关系匹配与拓扑结构对齐优化跨模态语义适配；在输出层通过响应约束确保融合结果的视觉一致性与语义完整性，缓解了师生网络模态偏好不匹配导致的信息失衡.此外，构建适配任务特性的轻量化CNN-Transformer双分支学生网络，兼顾全局信息建模与局部细节感知，增强对异构知识的接收整合能力.在MSRS、RoadScene、TNO和M3FD数据集上的实验结果表明，所提方法在三种结构差异显著的教师模型的指导下，互相关系数（Correlation Coefficient，CC）、峰值信噪比（Peak Signal-to-Noise Ratio，PSNR）、空间频率保持度（Sum of the Correlations of Differences，SCD）和结构相似性指数（Structural Similarity Index Measure，SSIM）四项指标均优于教师模型及现有方法，且模型参数量仅为0.077 2 M，服务器上推理时间仅为31.22 ms，在提升融合性能与蒸馏鲁棒性的同时，实现了融合网络的轻量化与实时性；同时模型在Jetson AGX Xavier边缘平台上推理时间仅为250.31 ms，具备良好的边缘部署能力与实际应用价值.

Abstract

Knowledge distillation transfers the representation capability of a complex teacher network to a lightweight student network

thereby enhancing model performance and deployment efficiency. However

existing knowledge distillation-based multimodal image fusion methods often neglect the heterogeneity of feature representations and modality preferences between teacher and student networks

as well as the inherent differences across modalities. This limitation results in inefficient knowledge transfer

insufficient semantic alignment

and degraded fusion performance. To address these issues

we propose an infrared and visible image fusion method based on heterogeneous model multi-level knowledge distillation. Specifically

a cross-layer knowledge transfer mechanism is designed: at the feature layer

attention is utilized to guide the precise transfer of infrared salient targets and visible-light textures; at the relationship layer

similarity-based relational matching and topological structure alignment are employed to enhance cross-modal semantic adaptation; and at the output layer

response constraints are applied to ensure both visual consistency and semantic integrity of the fused results

alleviating the information imbalance caused by mismatched modality preferences between teacher and student networks. In addition

we construct a task-adaptive lightweight CNN-Transformer dual-branch student network that simultaneously models global information and captures local details

thereby enhancing its ability to receive and integrate heterogeneous knowledge. Experimental results on the MSRS

RoadScene

TNO

and M3FD datasets demonstrate that under the guidance of three teacher models with significantly different architectures

the proposed method outperforms both the teacher models and state-of-the-art approaches in terms of correlation coefficient (CC)

peak signal-to-noise ratio (PSNR)

sum of the correlations of differences (SCD) and structural similarity index measure (SSIM) metrics

while requiring only 0.077 2 M parameters and achieving 31.22 ms inference time on a server platform. Moreover

the model maintains an inference time of 250.31 ms on the Jetson AGX Xavier edge platform

indicating strong suitability for edge deployment and practical applications.

关键词

Keywords

references

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