电镜图像的量子衍生协同式边缘滤波

doi:10.3969/j.issn.1000-1158.2022.08.09

摘要
图/表
参考文献(18)
相关文章 (15)

全文: PDF (4865 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对冠状病毒电镜图像特点及其轮廓提取、目标检测和噪声抑制的综合处理需求,研究了量子衍生边缘滤波机制及协同策略。从像素的双量子比特态表示与量子测量及结果检验方式出发,结合力场转换与量子衍生中值滤波,构建了量子衍生协同式边缘滤波框架及实施路线。实验分析了边缘滤波框架中的协同策略及融合效果,对比验证了量子测量结果检验方式的有效性。实验表明:冠状病毒电镜图像经过量子衍生协同式边缘滤波处理后,提升了形态学分析的质量条件。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	崔法毅

关键词 ：计量学, 电镜图像, 量子衍生协同式边缘滤波, 双量子比特态表示, 量子测量, 中值滤波算法, 力场转换

Abstract：According to characteristics of the coronavirus electron microscope image and the comprehensive processing requirements of contour extraction, target detection and noise suppression, the quantum-inspired edge filtering mechanism and collaborative strategy are studied.Starting from double qubit state representation of pixel and quantum measurement result inspection, the framework and implementation route of quantum-inspired collaborative edge filtering are constructed, combined with force field transform and quantum-inspired median filtering.The collaborative strategies and fusion effects in the edge filtering framework are analyzed experimentally, and the effectiveness of quantum measurement result inspection methods is verified comparatively.The experimental results show that electron microscope images of coronavirus have improved the quality conditions for morphological analysis after being processed by quantum-inspired collaborative edge filtering.

Key words： metrology electron microscore images quantum-inspired collaborative edge filtering double qubit state representation quantum measurement median filtering algorithm force field transform

收稿日期: 2021-10-19 发布日期: 2022-08-20

PACS:	TB96
	TB99

基金资助:河北省高等学校科学技术研究项目(QN2017363)

作者简介: 崔法毅(1982-),男,辽宁盖州人,燕山大学讲师,博士,主要从事量子信息处理、人工智能、深度学习研究。Email:fayi2001@ysu.edu.cn

引用本文:

崔法毅. 电镜图像的量子衍生协同式边缘滤波[J]. 计量学报, 2022, 43(8): 1027-1035.
CUI Fa-yi. Quantum-inspired Collaborative Edge Filtering of Electron Microscope Images. Acta Metrologica Sinica, 2022, 43(8): 1027-1035.

链接本文:

http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2022.08.09 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2022/V43/I8/1027

［1］张永卓, 王晶, 付博强, 等. 2019新型冠状病毒的核酸检测［J］. 计量学报, 2020, 41 (4): 393-398.
Zhang Y Z, Wang J, Fu B Q, et al. Nucleic Acid Detection of the SARS-Cov-2 ［J］. Acta Metrologica Sinica, 2020, 41 (4): 393-398.
［2］高原, 陈川, 王晶. 2019新型冠状病毒的抗原抗体检测［J］. 计量学报, 2020, 41 (5): 513-517.
Gao Y, Chen C, Wang J. Antigen and Antibody Detection of SARS-CoV-2 ［J］. Acta Metrologica Sinica, 2020, 41 (5): 513-517.
［3］王迪, 王志栋, 吴枭, 等. SARS-CoV-2基因组RNA标准物质的研制［J］. 计量学报, 2021, 42 (2): 259-264.
Wang D, Wang Z D, Wu X, et al. Development of Reference Material of SARS-CoV-2 Genomic RNA ［J］. Acta Metrologica Sinica, 2021, 42 (2): 259-264.
［4］Eldar Y C, Oppenheim A V. Quantum signal processing ［J］. IEEE Signal Processing Magazine, 2002, 19 (6): 12-32.
［5］谢可夫, 许悟生. 基于量子理论的图像中值滤波［J］. 计算机工程, 2013, 39 (1): 244-247.
Xie K F, Xu W S. Image median filtering baesd on Quantum theory ［J］. Computer Engineering, 2013, 39 (1): 244-247.
［6］谢可夫, 许悟生. 量子衍生图像分解和边缘检测［J］. 计算机应用, 2013, 33 (4): 1089-1091.
Xie K F, Xu W S. Quantum-inspired image decomposition and edge detection ［J］. Journal of Computer Applications, 2013, 33 (4): 1089-1091.
［7］付晓薇, 丁明跃, 周成平, 等. 基于量子概率统计的医学图像增强算法研究［J］. 电子学报, 2010, 38 (7): 1590-1596.
Fu X W, Ding M Y, Zhou C P, et al. Research on image enhancement algorithms of medical images based on quantum probability statistics ［J］. Acta Electronica Sinica, 2010, 38 (7): 1590-1596.
［8］付晓薇, 丁明跃, 蔡超, 等. 基于量子衍生参数估计的医学超声图像去斑算法［J］. 电子学报, 2011, 39 (4): 812-818.
Fu X W, Ding M Y, Cai C, et al. Despeckling of medical ultrasound images based on quantum-inspired parameters estimation ［J］. Acta Electronica Sinica, 2011, 39 (4): 812-818.
［9］崔法毅. 电镜图像的量子衍生增强算法［J］. 计量学报, 2021, 42 (12): 1585-1595.
Cui F Y. Quantum-inspired enhancement algorithms of electron microscope images ［J］. Acta Metrologica Sinica, 2021, 42 (12): 1585-1595.
［10］柯余仙, 毕思文. 基于量子衍生的遥感图像边缘检测方法［J］. 无线电工程, 2018, 48 (11): 966-971.
Ke Y X, Bi S W. Quantum-inspired remote sensing image edge detection methods ［J］. Radio Engineering, 2018, 48 (11): 966-971.
［11］袁素真, 毛峡, 陈立江, 等. 基于双量子比特态测量的量子自适应中值滤波［J］. 北京航空航天大学学报, 2013, 39 (11): 1443-1448.
Yuan S Z, Mao X, Chen L J, et al. Quantum adaptive median filtering based on dual qubits state measurement ［J］. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39 (11): 1443-1448.
［12］EI-ashry A M, Alrahmawy M F, Rashad M Z. Enhanced quantum inspired grey wolf optimizer for feature selection ［J］. International Journal of Intelligent Systems and Applications,2020, 12 (3): 8-17.
［13］Li Q C, Gkoumas D, Lioma C. Quantum-inspired multimodal fusion for video sentiment analysis ［J］. Information Fusion, 2021, 65: 58-71.
［14］Kolenderska S M, Vanholsbeeck F, Kolenderski P. Quantum-inspired detection for spectral domain optical coherence tomography ［J］. Optics Letters, 2020, 45 (13): 3443-3446.
［15］Bai L, Rossi L, Cui L, et al. A quantum-inspired similarity measure for the analysis of complete weighted graphs ［J］. IEEE Transactions on Cybernetics, 2020, 50 (3): 1264-1277.
［16］Tiwari K, Arya D K, Badrinath G S, et al. Designing palmprint based recognition system using local structure tensor and force field transformation for human identification ［J］. Neurocomputing, 2013, 116: 222-230.
［17］沈荻帆, 张育, 任佳. 基于低秩分解和改进的非局部平均的SAR图像相干斑抑制［J］. 信号处理, 2020,36 (3): 463-470.
Shen D F, Zhang Y, Ren J. A SAR image despeckling method based on low-rank decomposition and improved nonlocal means ［J］. Journal of Signal Processing, 2020,36 (3): 463-470.
［18］胡安康, 邱睿, 王文静, 等. 数字乳腺X射线摄影成像参数优化研究［J］. 原子能科学技术, 2021,55 (6): 961-967.
Hu A K, Qiu R, Wang W J, et al. Study on optimization of exposure parameter of digital mammography ［J］. Atomic Energy Science and Technology, 2021,55 (6): 961-967.