Infrared image generation with unpaired training samples

CAI Wei; JIANG Bo; JIANG Xinhao; YANG Zhiyong

doi:10.37188/OPE.20233124.3651

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Infrared image generation with unpaired training samples

Information Sciences | 更新时间：2026-09-11

- Infrared image generation with unpaired training samples
- Optics and Precision Engineering Vol. 31, Issue 24, Pages: 3651-3661(2023)
- 作者机构：
  
  火箭军工程大学兵器发射理论与技术国家重点学科实验室，陕西西安 710025
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233124.3651
  CLC： TP391
- Received：28 June 2022，
  
  Revised：2022-07-26，
  
  Published：25 December 2023
- 稿件说明：
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蔡伟,姜波,蒋昕昊等.非成对训练样本条件下的红外图像生成[J].光学精密工程,2023,31(24):3651-3661.

CAI Wei,JIANG Bo,JIANG Xinhao,et al.Infrared image generation with unpaired training samples[J].Optics and Precision Engineering,2023,31(24):3651-3661.
蔡伟,姜波,蒋昕昊等.非成对训练样本条件下的红外图像生成[J].光学精密工程,2023,31(24):3651-3661. DOI： 10.37188/OPE.20233124.3651.

CAI Wei,JIANG Bo,JIANG Xinhao,et al.Infrared image generation with unpaired training samples[J].Optics and Precision Engineering,2023,31(24):3651-3661. DOI： 10.37188/OPE.20233124.3651.

摘要

针对实测红外图像数据集构建难度大、测试制作成本高的问题，本文提出了一种非成对训练样本条件下的生成对抗网络VTIGAN，实现不同场景下可见光到红外（Visible-to-Infrared，VTI）的高质量图像转换。VTIGAN在transformer模块基础引入了一个新的生成器学习图像内容映射关系，通过重组目标风格特性实现图像风格转换，同时使用PathGAN作为判别器强化模型的图像细节信息生成能力，最后联合对抗损失、多层对比损失、风格相似性损失、同一性损失四种损失函数对模型训练过程进行约束。将VTIGAN与其他主流算法在可见光-红外数据集上进行了广泛的对比实验，同时使用峰值信噪比、结构相似度和Frechét inception distance 3个评价指标进行定量评估和主观定性评价。实验结果表明，VTIGAN相比于次优的UGATIT算法在PSNR，SSIM和FID三个指标上分别提升了3.1%，2.8%和11.3%，有效实现了非成对训练样本条件下可见光到红外的图像转换，且对于复杂场景的抗干扰能力更强，生成的红外图像清晰度高、细节特征完整、真实感强。

Abstract

To address the difficulties in constructing an infrared image dataset from measurements and the high cost of testing and production， this study proposes a generation countermeasure VTIGAN for unpaired training samples to achieve high-quality image conversion from visible-to-infrared in different scenarios. VTIGAN introduces a new generator inspired by the transformer module to learn the mapping relationship of image content， whereby image style conversion is realized by reorganizing the characteristics of the target style. PathGAN is used as a discriminator to strengthen the image detail information generation ability of the model. Finally， four loss functions， namely， resistance， multi-layer contrast， style similarity， and identity losses are combined to constrain the model training process. VTIGAN was compared with other mainstream algorithms in a wide range of experiments on visible infrared datasets. Three evaluation indicators， namely， peak signal-to-noise ratio （PSNR）， structural similarity （SSIM）， and Fréchet inception distance （FID）， were used for quantitative and subjective qualitative evaluation. The experimental results show that VTIGAN improves PSNR， SSIM， and FID by 3.1%， 2.8%， and 11.3%， respectively， compared with the suboptimal UGATIT algorithm， which effectively realizes image conversion from visible light to infrared under the condition of unpaired training samples， demonstrating stronger anti-interference ability for complex scenes. The generated infrared images have high definition， complete details， and strong realism.

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references

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