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微铣刀同轴全息图像增强方法

程亚亚 于化东 于占江 许金凯 张向辉

程亚亚, 于化东, 于占江, 许金凯, 张向辉. 微铣刀同轴全息图像增强方法[J]. 中国光学(中英文), 2020, 13(4): 705-712. doi: 10.37188/CO.2019-0217
引用本文: 程亚亚, 于化东, 于占江, 许金凯, 张向辉. 微铣刀同轴全息图像增强方法[J]. 中国光学(中英文), 2020, 13(4): 705-712. doi: 10.37188/CO.2019-0217
CHENG Ya-ya, YU Hua-dong, YU Zhan-jiang, XU Jin-kai, ZHANG Xiang-hui. Method of enhancing the quality of in-line holographic images for micro-milling tool[J]. Chinese Optics, 2020, 13(4): 705-712. doi: 10.37188/CO.2019-0217
Citation: CHENG Ya-ya, YU Hua-dong, YU Zhan-jiang, XU Jin-kai, ZHANG Xiang-hui. Method of enhancing the quality of in-line holographic images for micro-milling tool[J]. Chinese Optics, 2020, 13(4): 705-712. doi: 10.37188/CO.2019-0217

微铣刀同轴全息图像增强方法

doi: 10.37188/CO.2019-0217
基金项目: 国家重点研发计划(No.2018YFB1107403);中国“111”计划(No.D17017);吉林省科技发展计划(No.20190101005JH, No.20180201057GX)
详细信息
    作者简介:

    程亚亚(1995—),女,新疆石河子人,硕士研究生,2017年于长春理工大学获得学士学位,主要从事微纳技术方面的研究。E-mail: 15764341171@163.com

    于化东(1961—),男,吉林松原人,博士,教授,博士生导师,1983年、1988年于长春光学精密机械学院(现长春理工大学)分别获得学士、硕士学位,1998年于日本千叶大学获博士学位,主要从事精密超精密加工技术、微纳制造与检测技术方面的研究。E-mail: yuhuadong@cust.edu.cn

  • 中图分类号: O438.1

Method of enhancing the quality of in-line holographic images for micro-milling tool

Funds: (The National Key Research and Development Plan Project (No. 2018YFB1107403); The “111” Project of China (No. D17017); Jilin Province Scientific and Technological Development Program (No. 20190101005JH and No. 20180201057GX).)
More Information
  • 摘要: 数字同轴全息对刀技术中,再现像中的零级项和离焦共轭像会形成一个强而复杂的背景噪声作用在实像上,严重降低了再现像的质量。针对全息应用中的干扰像问题,提出了一种基于改进自蛇模型滤波的全息图像增强方法,改进后的自蛇模型令每次扩散中只根据初始图像的梯度来选择扩散力度。实验结果表明,改进后的自蛇模型能够避免在扩散过程中由于受大梯度背景噪声影响而出现的“伪轮廓”和边缘锯齿化,弥补了自蛇模型在全息图像应用中的不足。此外,与相位恢复法和多重再现法去干扰像效果相比较,本文提出的改进自蛇模型滤波法不仅对干扰像有更好的抑制作用,还能够增强刀具边缘,有利于实现微铣刀的数字全息对刀。

     

  • 图 1  数字同轴全息记录过程示意图

    Figure 1.  Schematic diagram of the recording process of digital in-line holography

    图 2  改进前后的自蛇模型处理结果。(a)原图;(b)改进前的自蛇模型扩散结果及局部放大图;(c)改进后的自蛇模型扩散结果及局部放大图

    Figure 2.  The diffusion results of self-snake model before and after improvement. (a) Original image; (b) diffusion result and local enlargement obtained by self-snake model; (c) diffusion result and local enlargement obtained by improved self-snake model

    图 3  模拟实验结果。(a)待测物体;(b)全息图;(c)再现像;(d)再现像的纵截面强度分布

    Figure 3.  Results of simulation experiments. (a) Tested object; (b) digital hologram; (c) reconstruction image; (d) intensity distribution of reconstruction image

    图 4  不同方法消除干扰像后的模拟物体再现像(左)及再现像第128行的强度分布(右)。(a)相位恢复法;(b)多重再现法;(c)自蛇模型滤波法;(d)改进自蛇模型滤波法

    Figure 4.  The reconstructed images of simulation object (left) that interferential image were eliminated by different approaches and corresponding intensity distributions in 128th line (right). (a) Phase retrieval approach; (b) multi-plane reproduction approach; (c) self-snake filtering approach; (d) improved self-snake filtering approach

    图 5  数字全息实验装置

    Figure 5.  Digital holographic experiment device

    图 6  刀具全息图(a)及再现像(b)

    Figure 6.  Hologram (a) and reconstruction image (b) of the milling tool

    图 7  不同方法消除干扰像后的微铣刀再现像(左)及对应截面的强度分布(右)。(a)相位恢复法;(b)多重再现法;(c)自蛇模型滤波法;(d)改进自蛇模型滤波法

    Figure 7.  The reconstructed images of the micro-milling tool (left) that interferential image were eliminated by different approaches and their intensity distributions (right). (a) Phase retrieval approach; (b) multi-plane reproduction approach; (c) self-snake filtering approach; (d) improved self-snake filtering approach

    图 8  再现像轮廓提取结果。(a)相位恢复法;(b)多重再现法;(c)自蛇模型滤波法;(d)改进自蛇模型滤波法

    Figure 8.  Results of contour extraction from reconstructed images. (a), (b), (c), (d) are reconstructed images obtained by phase retrieval approach, multi-plane reproduction approach, self-snake filtering approach and improved self-snake filtering approach, respectively

    表  1  各种去干扰像方法性能比较

    Table  1.   Performance comparison of different interference removal methods

    MethodSNR/dBPSNR/dB
    Phase retrieval0.28124.365
    Multi-plane reproduction0.01924.103
    Self-snake filtering8.30432.388
    Improved self-snake filtering8.43332.517
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-11-13
  • 修回日期:  2019-12-09
  • 刊出日期:  2020-08-01

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