低成本红外热成像系统研制
Development of Low Cost Infrared Thermal Imaging System
DOI: 10.12677/JISP.2018.72009, PDF,    科研立项经费支持
作者: 王雪楠*, 毛 杰, 李佳瑶, 崔笑宇：东北大学中荷生物医学与信息工程学院，辽宁 沈阳
关键词: 红外热成像；FPGA；超分辨率重建；Infrared Thermal Imaging； FPGA； Super-Resolution Reconstruction
摘要: 本文提出一种基于红外热成像摄像头的低成本红外图像采集和处理系统。以Xilinx公司的ZYNQ-7000系列SoC开发板为核心处理元件，采用Raspberry Pi作为远红外摄像头的适配器，选用FILR Lepton LWIR为图像获取设备。在LWIR获取到图像后通过SPI接口传入Raspberry Pi，之后利用TCP套接字将图像送到SoC的FPGA上进行处理。借由FPGA突出的并行运算特性，原图像可迅速得到算法改良后的图像。本文以图像超分辨率重建以及实时CANNY算子为例进行了实验。实验表明，本系统具备低成本、低功耗与易用等特性。
Abstract: This paper presents a low-cost infrared thermal camera image processing system. The program takes the ZYNQ-7000 series SoC development board produced by Xilinx as the core processing element, whereby the Raspberry Pi is used as an adapter for the far-infrared camera and the FILR Lepton LWIR is selected as the image acquisition device. After the LWIR gets the image, it passes the Raspberry Pi through the SPI interface, and then uses the TCP socket to send the image to the SoC FPGA for processing. By highlighting the parallelism of the FPGA features, the original image can be quickly improved algorithm image. This paper, taking image super-resolution reconstruction and real-time CANNY operator as an example, conducted experiments, which show that the system has the characteristics of low cost, low power consumption and ease of use.
文章引用：王雪楠, 毛杰, 李佳瑶, 崔笑宇. 低成本红外热成像系统研制[J]. 图像与信号处理, 2018, 7(2): 74-84. https://doi.org/10.12677/JISP.2018.72009

参考文献

[1] 孙志君. 红外焦平面阵列技术的未来二十年[J]. 传感器世界, 2002, 8(11): 1-8.
[2] 彭焕良. 热成像技术发展综述[J]. 激光与红外, 1997, 27(3): 131-136.
[3] 章睿. 便携式非制冷红外成像设备的设计[J]. 国外电子测量技术, 2007, 26(4): 65-67.
[4] 高翔宇. 基于Linux操作系统下的TCP/IP网络通信研究与应用[J]. 现代电子技术, 2006, 233(18): 74-76.
[5] 花歆悦. 基于压缩感知的声纳图像超分辨率重建算法[J]. 微型机与应用, 2015, 34(13): 49-52.
[6] Ng, M.K. and Bose, N.K. (2003) Mathematical Analysis of Super-Resolution Methodology. IEEE Signal Processing Magazine, 20, 62-74.
https://doi.org /10.1109/MSP.2003.1203210
[7] Candès, E., Romberg, J. and Tao, T. (2006) Robust Uncertainty Principles: Exact Signal Re-construction from highly Incomplete Frequency Information. IEEE Transactions on Information Theory, 52, 489-509.
https://doi.org /10.1109/TIT.2005.862083
[8] Donoho, D.L. (2006) Compressed Sensing. IEEE Transactions on In-formation Theory, 52, 1289-1306.
https://doi.org /10.1109/TIT.2006.871582
[9] 刘娟. 基于压缩感知的单幅图像超分辨率重建算法研究[D]: [硕士学位论文]. 成都: 电子科技大学, 2012.
[10] Yang, J., Wright, J., Huang, T. and Ma, Y. (2008) Image Super-Resolution as Sparse Representation of Raw Image Patches. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, 23-28 June 2008, 1-8.
[11] 林卉, 赵长胜, 舒宁. 基于Canny算子的边缘检测及评价[J]. 黑龙江工程学院学报, 2003, 17(2): 3-6.