一种水上目标可见光图像生成红外图像的方法

徐亦欣; 胡清平; 熊张; 张晓晖

一种水上目标可见光图像生成红外图像的方法

海军工程大学兵器工程学院, 湖北武汉 430034

详细信息

作者简介:
徐亦欣（1995-），女，硕士研究生，主要研究方向为军用目标探测与信息感知技术。E-mail：724902343@qq.com

通讯作者:
张晓晖（1965-），女，教授，主要研究方向为军用光电工程。E-mail：xiaoyan6481@sina.com

中图分类号: TP391
计量
- 文章访问数: 260
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- 被引次数: 0
出版历程
- 收稿日期: 2021-11-12
- 修回日期: 2022-04-01
- 刊出日期: 2022-09-20

Method for Generating Infrared Image from Visible Image of Water Surface Targets

College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China

摘要

摘要: 红外警戒系统、红外成像制导导弹等军事装备在进行性能评估和模拟训练过程中都需要大量红外仿真图像，但目前红外仿真软件普遍存在生成红外仿真图像逼真度差、软件普适性不好等问题，且国外技术封锁造成我国红外仿真软件发展缓慢。因此，针对国内可见光图像仿真技术日趋成熟的现状，为提高红外仿真图像质量，本文提出了一种采用循环生成对抗网络、由可见光图像生成红外仿真图像的方法，并通过实验验证该算法是有效可行的。该算法首先通过区域生长算法从采集的可见光图像中提取水上目标，建立了水上目标可见光图像生成红外图像的训练数据集；然后利用训练好的网络生成红外仿真图像。测试实验表明，采用这种方法所生成的水上目标红外仿真图像视觉效果接近真实红外图像，可实际应用于海军红外军事装备模拟试验和训练系统。
- 红外图像仿真 /
- 图像风格迁移 /
- 循环生成对抗网络 /
- 区域生长算法
Abstract: Military equipment, such as infrared warning systems and infrared image-guided missiles, requires a number of infrared simulation images during performance evaluation and simulation training. However, the infrared simulation image generated by infrared simulation software has some problems, such as poor fidelity and poor universality. The domestic infrared simulation technology is hampered by foreign technology blockade. Currently, the development situation is difficult in meeting the requirement of the application. To solve this problem, this experiment proposed a visible-infrared image transform method. First, the region seeds growing (RSG) algorithm was used to extract the water target from the collected image. Subsequently, the visible and infrared image water target datasets were established. Finally, the trained cycle generative adversarial network (CycleGAN) was used to generate the infrared simulation image from the visible image. The test results show that the visual effect of the infrared simulation image generated by this method is close to that of the real infrared image and can be applied to the naval infrared military equipment simulation test and training system.
- infrared image simulation /
- image style migration /
- CycleGAN /
- RSG

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图 1 循环生成对抗网络原理

Figure 1. Cycle generative adversarial network model

下载: 全尺寸图片幻灯片

图 2 可见光、红外图像预处理

Figure 2. Image preprocessing of visible images and infrared images

下载: 全尺寸图片幻灯片

图 3 原始可见光图像经过不同迭代次数生成的红外仿真图像

Figure 3. Original visible images and infrared simulation images generated in different epochs

下载: 全尺寸图片幻灯片

图 4 红外图像仿真测试结果

Figure 4. Infrared image simulation test results

下载: 全尺寸图片幻灯片

图 5 验证方案示意

Figure 5. Validation scheme of proposed method

下载: 全尺寸图片幻灯片

图 6 图像评价指标参数比较：(a) 结构相似性指数；(b)信息熵；(c) 平均梯度；(d) 边缘强度；(e) 标准差；(f) 空间频率

Figure 6. Comparison of image performance indicators: (a) Structural similarity index measure (b) Entropy (c) Average gradient (d) Edge intensity (e) Standard deviation (f) Spatial frequency

下载: 全尺寸图片幻灯片

表 1 模型参数值

Table 1. Model parameters

Parameters Learning rate Epoch N_epochs N_epochs decay Beta1 Beta2 Decay iters

Value 0.0002 200 100 100 0.5 0.999 50

下载: 导出CSV

参考文献(11)

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