自适应激光共焦高速扫描显微成像错位校正算法
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摘要
往复式逐行扫描是一种提高激光共焦扫描显微成像帧速的有效方式,然而随着帧速的提高,这种扫描方式在图像重构时会带来更严重的图像错位。根据高速振镜运动特性,分析了激光共焦高速扫描显微成像系统逐行扫描下的重构图像错位原理,设计了基于形态学梯度的重构图像错位评价算法,并且结合搜索错位评价最小点的单目标约束粒子群算法实现了重构图像错位校正。经实验验证,该算法适用于成像帧速高达300 frame/s的重构图像错位校正;与未进行错位校正的图像相比,校正后的成像分辨率提高了68.83%,同时该算法能够适用于不同振镜搭配方式和不同扫描帧速的图像重构。
The reciprocating progressive scanning is an effective way to increase the imaging frame rate of laser confocal scanning microscopic imaging. However, serious image dislocations during image reconstruction are introduced as the frame rate increases. According to the motion characteristics of a high-speed galvanometer, the dislocation principle of reconstructed images by the laser confocal high-speed scanning microscopic imaging system is analyzed. A dislocation evaluation algorithm of reconstructed images is designed based on the morphological gradients. In addition, the single-objective constrained particle swarm algorithm is used to realize the dislocation correction of reconstructed images by combining with the search for the minimum point of dislocation evaluation.The experimental results show that the proposed algorithm is suitable for the dislocation correction of reconstructed images even when the imaging frame is up to 300 frame/s. Compared with that of the original images without dislocation correction, the imaging resolution after correction increases by 68.83%. Moreover, this algorithm is also suitable for the image reconstruction under different mirror combinations and different scanning frame rates.
The reciprocating progressive scanning is an effective way to increase the imaging frame rate of laser confocal scanning microscopic imaging. However, serious image dislocations during image reconstruction are introduced as the frame rate increases. According to the motion characteristics of a high-speed galvanometer, the dislocation principle of reconstructed images by the laser confocal high-speed scanning microscopic imaging system is analyzed. A dislocation evaluation algorithm of reconstructed images is designed based on the morphological gradients. In addition, the single-objective constrained particle swarm algorithm is used to realize the dislocation correction of reconstructed images by combining with the search for the minimum point of dislocation evaluation.The experimental results show that the proposed algorithm is suitable for the dislocation correction of reconstructed images even when the imaging frame is up to 300 frame/s. Compared with that of the original images without dislocation correction, the imaging resolution after correction increases by 68.83%. Moreover, this algorithm is also suitable for the image reconstruction under different mirror combinations and different scanning frame rates.
引文
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[14] Wang D D, Xu Y B, Chen X X, et al. Absolute displacement measurement with point-diffraction interferometer based on quick searching particle swarm optimization algorithm[J]. Acta Optica Sinica, 2016, 36(1):0112001.王道档,徐杨波,陈茜茜,等.基于快速搜索粒子群算法的点衍射干涉绝对位移测量方法[J].光学学报,2016, 36(1):0112001.
[15] Gao H X, Luo L, Luo Y H, et al. Improved stochastic CT reconstruction based on particle swarm optimization for limited-angle sparse projection data[J]. Acta Optica Sinica,2018, 38(1):0111003.高红霞,罗澜,骆英浩,等.角度受限下稀疏投影数据的改进粒子群优化随机CT重建[J].光学学报,2018, 38(1):0111003.
[2] Di L, Wang J Y. Research of using laser scanning confocal microscopy Leica SP8 STED 3X[J]. China Medical Devices, 2017, 32(2):9-15.狄伶,王瑾晔.Leica SP8 STED 3X激光扫描共聚焦显微镜的使用研究[J].中国医疗设备,2017, 32(2):9-15.
[3] Mi X. Research on key techniques of the line scanning confocal biochip fluorescence detection device[D]. Beijing:Beijing Institute of Technology,2016.米雪.线扫描型共聚焦生物芯片荧光检测关键技术研究[D].北京:北京理工大学,2016.
[4] Su J F. Moving target detecting and recognition under the laser scanning[D]. Chengdu:University of Electronic Science and Technology of China, 2014.苏靖峰.基于激光扫描的运动目标检测与识别[D].成都:电子科技大学,2014.
[5] Guo Y J, Tang S X, Jiang X Q, et al. Damage inspection of optical surface based on galvanometer scanning[J]. Acta Optica Sinica, 2017,37(6):0612003.郭亚晶,唐顺兴,姜秀青,等.基于振镜扫描方式的光学元件表面损伤检测[J].光学学报,2017, 37(6):0612003.
[6] Wei T D. Key technologies research in confocal laser scanning optical microscopy imaging[D].Changchun:University of Chinses Academy of Sciences(Changchun Institute of Optics, Fine Mechanics and Physics), 2014.魏通达.共聚焦激光扫描光学显微成像关键技术研究[D].长春:中国科学院大学(长春光学精密机械与物理研究所),2014.
[7] Leybaert L, de Meyer A, Mabilde C, et al. A simple and practical method to acquire geometrically correct images with resonant scanning based line scanning in a custom-built video-rate laser scanning microscope[J]. Journal of Microscopy, 2005, 219(3):133-140.
[8] Ye Q. A study of high speed galvanometer and practise[D]. Wuhan:Huazhong University of Science&Technology, 2004.叶乔.高速振镜理论研究及实践[D].武汉:华中科技大学,2004.
[9] Xiong D X, Liu Y, Liang Y, et al. Correction of distortion in microscopic imaging with resonant scanning[J]. Optics and Precision Engineering,2015, 23(10):2971-2979.熊大曦,刘云,梁永,等.共振扫描显微成像中的图像畸变校正[J].光学精密工程,2015, 23(10):2971-2979.
[10] Zhao W Q, Ren L L, Sheng Z, et al. Beam deflection scanning for laser confocal microscopy[J].Optics and Precision Engineering, 2016, 24(6):1257-1263.赵维谦,任利利,盛忠,等.激光共焦显微光束的偏转扫描[J].光学精密工程,2016, 24(6):1257-1263.
[11] Li Y, Zuo M J, Chen Y, et al. An enhanced morphology gradient product filter for bearing fault detection[J]. Mechanical Systems and Signal Processing, 2018, 109:166-184.
[12] Tseng C S, Wang J H. Perceptual edge detection via entropy-driven gradient evaluation[J]. IET Computer Vision, 2016, 10(2):163-171.
[13] Zhang L X, Sun H Y, Guo H C, et al. Auto focusing algorithm based on largest gray gradient summation[J]. Acta Photonica Sinica, 2013, 42(5):605-610.张来线,孙华燕,郭惠超,等.基于图像灰度梯度最大值累加的自动调焦算法[J].光子学报,2013, 42(5):605-610.
[14] Wang D D, Xu Y B, Chen X X, et al. Absolute displacement measurement with point-diffraction interferometer based on quick searching particle swarm optimization algorithm[J]. Acta Optica Sinica, 2016, 36(1):0112001.王道档,徐杨波,陈茜茜,等.基于快速搜索粒子群算法的点衍射干涉绝对位移测量方法[J].光学学报,2016, 36(1):0112001.
[15] Gao H X, Luo L, Luo Y H, et al. Improved stochastic CT reconstruction based on particle swarm optimization for limited-angle sparse projection data[J]. Acta Optica Sinica,2018, 38(1):0111003.高红霞,罗澜,骆英浩,等.角度受限下稀疏投影数据的改进粒子群优化随机CT重建[J].光学学报,2018, 38(1):0111003.