• Laser & Optoelectronics Progress
  • Vol. 61, Issue 8, 0837012 (2024)
Jiaguang Huang1, Zhenming Yu2、*, Guojin Peng2, Hui Gan2, and Lü Meini2
Author Affiliations
  • 1College of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, Guangxi , China
  • 2Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou 543002, Guangxi , China
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    DOI: 10.3788/LOP230799 Cite this Article Set citation alerts
    Jiaguang Huang, Zhenming Yu, Guojin Peng, Hui Gan, Lü Meini. Microscopic Image Stitching Algorithm Based on Stage Motion Information[J]. Laser & Optoelectronics Progress, 2024, 61(8): 0837012 Copy Citation Text show less

    Abstract

    Traditional non-real-time image stitching methods can easily lead to global stitching interruption due to local image misalignment. In addition, microscopic images have numerous similar microstructures, causing problems such as long feature detection time and high misalignment rate. To address these issues, a microscopic image prediction stitching algorithm based on carrier stage motion information is proposed. First, the size of the overlapping area between adjacent images is determined by controlling the XY axis movement distance of the electric carrier stage. The accelerated robust feature algorithm is then used to detect feature points in the overlapping area of the image. Second, the range of feature points to be matched is predicted based on the position relationship of the images, and the feature point with the minimum Euclidean distance is selected within the predicted range for matching. Finally, matching point pairs are coarsely screened by the slope of the matching feature points, and precise matching is performed using the random sample consensus algorithm to calculate the homography matrix and complete the image stitching. The improved weighted average algorithm is used to fuse the stitched images. Experimental results show that the proposed algorithm achieves a superior matching rate improvement of 7.95% to 26.52% compared to those obtained via the brute force and fast library for approximate nearest neighbors algorithms, effectively improving the registration accuracy. Moreover, at a resolution of 1600×1200, the multi-image stitching rate of 2 frame·s-1 achieves better results than those obtained by the AutoStitch software.
    F=AΓ
    P=Fσ
    S=NP
    S=NσΓA
    d(pn,pm)=i=164(xi-yi)2
    Si=NM-1100%
    Pi'=D-(Si-Pi)
    d(Pi,Pj)=i=164(xi-yi)2
    Pi(xi,yi)=PjMINdi(Pi,Pj)
    ki=yj-yixj+w-xi
    B'=HB
    x'y'1=h0h1h2h3h4h5h6h71xy1
    x'y'1=00h200h5h6h71xy1
    B'=h0h1h2h3h4h5h6h7110dx01dy001B
    I(x,y)=I1(x,y),(x,y)I1a1I1(x,y)+a2I2(x,y),(x,y)I1I2I2(x,y),(x,y)I2
    a1=x-XlXr-Xla2=Xr-xXr-Xl
    θ=π2x-XlXr-Xl
    a1=cos2θ
    a2=sin2θ
    cos2θ+sin2θ=1
    I(x,y)=I1(x,y),(x,y)I1a1I1(x,y)+a2I2(x,y),(x,y)Xl-Xra3I1(x,y)+a4I2(x,y),(x,y)Yu-Yb12(a1+a3)I1(x,y)+12(a2+a4)I2(x,y),(x,y)(Xl-Xr)(Yu-Yb)I2(x,y),(x,y)I2
    a3=cos2π(y-Yu)2(Yb-Yu)a4=sin2π(y-Yu)2(Yb-Yu)
    Pi,x=Pi-1,x+Wi-1-wPi,y=Hi-1-h
    Pi,x=Pi-1,x-(Wi-w)Pi,y=Hi-1-h
    Pi,x=Wi-1-wPi,y=Pi-1,y-(Hi-h)
    Pi,x=Pi-1,xPi,y=Pi-1,y-(Hi-h)
    Pi,x=Wi-1-wPi,y=Hi-1-h,
    Pi,x=Pi-1,xPi,y=Hi-1-h
    Jiaguang Huang, Zhenming Yu, Guojin Peng, Hui Gan, Lü Meini. Microscopic Image Stitching Algorithm Based on Stage Motion Information[J]. Laser & Optoelectronics Progress, 2024, 61(8): 0837012
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