Super-resolution reconstruction of micro-scanning images

被引：0
|
作者：
Zhao H.-G. [1 ,2 ]
Qu H.-S. [3 ]
Wang X. [4 ]
Shang Y. [1 ,5 ]
Liu L.-G. [4 ]
Han S.-W. [4 ]
Meng S. [6 ]
Wang P. [4 ]
机构：
[1] College of Aerospace Science and Engineering, National University of Defense Technology, Changsha
[2] Shenyang Aircraft Design and Research Institute, Aviation Industry Corporation of China, Ltd, Shenyang
[3] School of Computer Science and Technology, Xidian University, Xi'an
[4] Department of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun
[5] Hunan Provincial Key Laboratory of Image Measurement and Visual Navigation, Changsha
[6] IAE Industrial Group Co., Ltd., Shanghai
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2021年 / 29卷 / 10期
关键词：
Image processing; Micro-scan; Super-resolution; Target recognition;
D O I：
10.37188/OPE.20212910.2456
中图分类号：
学科分类号：
摘要：
To improve the target recognition distance of the airborne electro-optical reconnaissance equipment of a UAV, this study has developed a high-speed micro-scanning super-resolution core component based on an actual engineering project. The real-time super-resolution reconstruction algorithm is implemented in the embedded platform GPU-TX2i. First, the micro-scanning super-resolution core component moves according to the preset step size and frequency to obtain a continuous image sequence with sub-pixel deviation. Then, the image super-resolution reconstruction algorithm is used based on probability distribution to process the acquired four continuous images into higher resolution images. The experimental results show that the image sequence output achieved by the detector with a frame rate of 120 fps and a resolution of 640×512 is reconstructed via super-resolution and becomes an image sequence with a frame rate of 30 fps and a resolution of 1 280×1024. After super-resolution reconstruction, the effective spatial resolution of the image is increased by 78.2% and target recognition distance is increased by 43.3%. The reconstruction time of a high-resolution image is approximately 33 ms. Furthermore, the micro-scanning super-resolution core component's micro-scan response time is < 1.0 ms and the accuracy in place is < 0.3 μm (corresponding to approximately 0.03 pixels). These results meet the real-time and precision requirements of airborne electro-optical reconnaissance equipment. © 2021, Science Press. All right reserved.
引用
收藏
页码：2456 / 2464
页数：8
相关论文
共 13 条
  • [1] WILLIAM F., Recent Progress in Microscan Resolution Enhancement, Northrop grumman corp baltimore md electronic sensors and systems div, (1999)
  • [2] JEAN F, CHEVRETTE P C., Realization of a fast microscanning device for infrared focal plane arrays, Realization of a fast microscanning device for infrared focal plane arrays, (1996)
  • [3] JOHN G S, WAN W H, HARRIS V, Et al., Compact airborne staring FPA sensor with microscanning, Compact airborne staring FPA sensor with microscanning, (1996)
  • [4] JIN W Q, CHEN Y N, WANG X, Et al., Scanning type sub-pixel thermal imaging algorithm taking account of detector filling rate and micro-scanning contraposition bias, Journal of Infrared and Millimeter Waves, 27, 4, pp. 308-312, (2008)
  • [5] OLSON C, THEISEN M, PACE T, Et al., Model development and system performance optimization for staring infrared search and track (IRST) sensors, SPIE Defense+Security. Proc SPIE 9820, Infrared Imaging Systems, 9820, pp. 63-72, (2016)
  • [6] BRAY M, PANELLA I., Research strategy for the electro-optics sensors domain of the Materials and Components for Missiles Innovative Technology Partnership, SPIE Defense, Security, and Sensing. Proc SPIE7668, 7668, pp. 288-298, (2010)
  • [7] MILLERJOHN L., WILTSE J M., Benefits of microscan for staring infrared imagers, Benefits of microscan for staring infrared imagers, pp. 127-138, (2004)
  • [8] WANG J L, FU X S, HUANG ZH CH, Et al., Multi-type cooperative targets detection using improved YOLOv2 convolutional neural network, Opt. Precision Eng, 28, 1, pp. 251-260, (2020)
  • [9] FAN L L, ZHAO H W, ZHAO H Y, Et al., Survey of target detection based on deep convolutional neural networks, Opt. Precision Eng, 28, 5, pp. 1152-1164, (2020)
  • [10] WAGNER R S, WAAGEN D E, CASSABAUM M L., Image superresolution for improved automatic target recognition, Defense and Security. Proc SPIE 5426, Automatic Target Recognition XIV, 5426, pp. 188-196, (2004)
12