基于FPGA的红外镜头自动聚焦技术研究与实现

孙少伟; 杨粤涛; 杨炳伟; 万安军; 钟海林

基于FPGA的红外镜头自动聚焦技术研究与实现

苏州长风航空电子有限公司，江苏苏州 215001

详细信息

作者简介:
孙少伟（1982-），男，硕士，高级工程师，主要从事图像处理方面研究。E-mail: shaowei_sun171@sina.com

中图分类号: THB11
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- 文章访问数: 211
- HTML全文浏览量: 33
- PDF下载量: 87
- 被引次数: 0
出版历程
- 收稿日期: 2020-11-07
- 修回日期: 2021-03-24
- 刊出日期: 2021-05-22

Research and Implementation of Infrared Lens Auto-focus Technology Based on Field Programmable Gate Array

AVIC Suzhou Changfeng Avionics Co., Ltd., Suzhou 215001, China

摘要

摘要: 自动聚焦技术在红外热像仪监控领域有着非常重要的作用。目前业内红外自动聚焦技术存在聚焦成功率低、架构复杂、聚焦速度慢等状况，为此提出了一种基于FPGA的红外镜头自动聚焦技术，采用单FPGA实现了红外图像处理显示和自动聚焦的功能。鉴于红外图像普遍存在竖条纹噪声和随机噪声等特点，本文在实现聚焦过程中对红外清晰度评价算法和爬山算法做了改进和优化。实验结果表明本文提出的算法和实现方式能够很好地实现红外镜头的自动聚焦，同时具备集成度高、聚焦速度快、成功率高等特点，有比较广泛的应用前景。
- 红外 /
- 自动聚焦 /
- FPGA /
- 清晰度评价 /
- 爬山算法
Abstract: Autofocus technology plays an important role in the field of infrared thermal imager monitoring. At present, there exist some problems with infrared auto focusing technology, such as low success rate, complex architecture and low focusing speed. Therefore, this study proposes an auto focusing technology of infrared lens based on FPGA, which realizes the functions of infrared image processing, display, and auto focusing with a single FPGA. In view of the common characteristics of vertical stripe noise and random noise in infrared images, this study improves and optimizes the infrared definition evaluation algorithm and mountain climbing algorithm in the focusing process. The experimental results show that the algorithm and implementation method proposed in this study can help infocusing on the infrared lens remarkably. Meanwhile, the proposed method has characteristics such as high integration, fast focusing speed, and high success rate, and thus has wide application prospects.
- infrared /
- autofocus /
- FPGA /
- definition evaluation /
- mountain climbing algorithm

HTML全文

图 1 自动聚焦红外热像仪实物图

Figure 1. Autofocus infrared thermal imager

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图 2 红外热像仪主要架构

Figure 2. Main structure of infrared thermal imager

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图 3 自动聚焦流程

Figure 3. Auto focus process

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图 4 利用3×3掩码计算梯度能量

Figure 4. Calculation of gradient energy using 3×3 mask

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图 5 在1/3中心窗格内选用3×5掩模示意图

Figure 5. Select 3×5 mask in 1/3 center pane

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图 6 利用FIFO和寄存器缓存3×3掩模数据

Figure 6. Cache 3×3 mask data by FIFO and register

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图 7 爬山算法示意

Figure 7. Hill climbing algorithm

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图 8 红外图像聚焦的爬山示意

Figure 8. Hill climbing of infrared image focusing

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图 9 本文提出的改进爬山算法在FPGA PS端实现流程

Figure 9. The improved hill climbing algorithm implemented on FPGA PS

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图 10 本文提出的自动聚焦后效果与其他方案效果对比

Figure 10. The effect of autofocus proposed in this paper is compared with other schemes

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图 11 两种区域像素求梯度能量值的图像显示对比

Figure 11. Comparison of image display based on gradient energy of two regions

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图 12 三种算法爬山过程电机转动聚焦曲线

Figure 12. Focusing curves of motor rotation with three algorithms for mountain climbing

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图 13 自动聚焦用时分析

Figure 13. Time spent on autofocus

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表 1 四幅画面的灰度方差值

Table 1. Gray variance of four pictures

Picture	Picture description	Gray variance
Fig. 10(b)	Original infrared image	2564.8
Fig. 10(c)	Picture focused of Document^[2]	2682.6
Fig. 10(d)	Picture focused of Document^[4]	2735.7
Fig. 10(e)	Picture focused of this article	2842.2

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表 2 FPGA资源消耗对比

Table 2. Comparison of FPGA resource consumption

	Whole area calculation	1/3area calculation	FPGA total resource
LUT(num)	19405	9814	78600
LUTRAM(num)	6408	2061	26600
FF(num)	84852	13188	157200
Block RAM(num)	165	90	265
DSPs(num)	32	8	400

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参考文献(8)

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