一种双分支特征交互融合的高效红外图像彩色化方法

陈宇; 詹伟达; 蒋一纯; 朱德鹏; 韩登

doi:10.7652/xjtuxb202508020

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一种双分支特征交互融合的高效红外图像彩色化方法

—————————————————————— | 更新时间：2025-07-30

- 一种双分支特征交互融合的高效红外图像彩色化方法
- An Efficient Infrared Image Colorization Method Based on Dual-Branch Feature Interaction Fusion
- 西安交通大学学报 2025年59卷第8期页码：211-222
- 作者机构：
  
  1.长春理工大学电子信息工程学院， 130022，长春
  2.吉林省知行物联网研究院有限公司， 130117，长春
- 作者简介：
  
  陈宇（1998—），男，博士生
  詹伟达，男，教授，博士生导师。
- 基金信息：
  
  吉林省重点研发计划资助项目(20240302029GX);吉林省发展和改革委员会创新能力建设专项资助项目(2024C021-8)
- DOI：10.7652/xjtuxb202508020
  中图分类号： TN911.73;TP391
- 收稿：2025-02-21，
  
  网络首发：2025-03-19，
  
  纸质出版：2025-08-10
- 稿件说明：
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陈宇, 詹伟达, 蒋一纯, 等. 一种双分支特征交互融合的高效红外图像彩色化方法[J]. 西安交通大学学报, 2025,59(8):211-222.

CHEN Yu, ZHAN Weida, JIANG Yichun, et al. An Efficient Infrared Image Colorization Method Based on Dual-Branch Feature Interaction Fusion[J]. Journal of Xi’an Jiaotong University, 2025, 59(8): 211-222.
陈宇, 詹伟达, 蒋一纯, 等. 一种双分支特征交互融合的高效红外图像彩色化方法[J]. 西安交通大学学报, 2025,59(8):211-222. DOI： 10.7652/xjtuxb202508020.

CHEN Yu, ZHAN Weida, JIANG Yichun, et al. An Efficient Infrared Image Colorization Method Based on Dual-Branch Feature Interaction Fusion[J]. Journal of Xi’an Jiaotong University, 2025, 59(8): 211-222. DOI： 10.7652/xjtuxb202508020.

摘要

针对现有的红外图像彩色化方法在全局特征捕获和计算复杂度方面存在显著局限性的问题，提出了一种双分支特征交互融合的高效红外图像彩色化方法。设计双分支编码器，通过局部特征提取分支获取局部空间上下文信息，确保细粒度特征的捕获，并通过全局特征提取分支获取全局特征，满足对长程依赖的需求。设计交互融合模块，对两个分支提取到的特征进行有效整合，显著增强了模型的整体性能。在解码器部分提出上下文聚合模块，进一步优化多尺度语义特征的聚合能力，改善了彩色化结果的边缘清晰度和细节表现力。在KAIST和FLIR数据集上进行广泛实验验证，结果表明：与现有方法相比，所提方法在两个数据集上均具有更高的彩色化质量，峰值信噪比分别达到28.645、30.459 dB，结构相似度达到0.507、0.725，均优于对比方法，且有效性和先进性也得到了验证。研究结果可为提升红外图像的可读性与可解释性以及提高夜视与恶劣环境下的观测能力提供参考。

Abstract

To address the significant limitations of existing infrared image colorization methods in global feature capture and computational complexity

an efficient infrared image colorization method based on dual-branch feature interaction fusion is proposed. A dual-branch encoder is designed

where the local feature extraction branch captures local spatial context information to ensure fine-grained feature acquisition

while the global feature extraction branch obtains global features to meet long-range dependency requirements. An interaction fusion module is developed to effectively integrate features extracted from both branches

significantly enhancing the model's overall performance. In the decoder part

a context aggregation module is proposed to further optimize multi-scale semantic feature aggregation

improving edge clarity and detail representation in the colorization results. Extensive experimental validation on the KAIST and FLIR datasets demonstrates that compared to existing methods

the proposed approach achieves superior colorization quality on both datasets

with peak signal-to-noise ratios reaching 28.645 dB and 30.459 dB

and structural similarity scores of 0.507 and 0.725

respectively

outperforming all comparative methods. The effectiveness and advancement of the method are thus verified. The research findings provide valuable references for enhancing the readability and interpretability of infrared images

as well as improving observation capabilities in night vision and harsh environments.

关键词

Keywords

references

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