电镜图像的量子衍生增强算法

doi:10.3969/j.issn.1000-1158.2021.12.06

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Abstract Under the framework of quantum information processing, the processing procedures of existing quantum-derived image enhancement algorithms are summarized from the aspects of pixel grayscale normalization, pixel quantum state representation, quantum measurement, and measurement result mapping. Taking the SARS-CoV-2 new coronavirus electron microscope image enhancement as a sample, combined with experimental analysis, the weighting method of the quantum derived enhancement operator is improved, and a non-iterative determination operator for the optimized value of the adjustable parameter of the gray scale transformation is proposed. The experimental results show that the quantum-derived enhancement algorithm comprehensively considers the global and local information of the electron microscope image, takes into account the adjustment of the contrast and brightness of the electron microscope image, and enhances the image with clear details and appropriate brightness.

Key words： metrology electron microscope image quantum-inspired image enhancement virus morphology analysis non-iterative parameter optimization enhancement operator weighting SARS-CoV-2

Received: 09 April 2021 Published: 06 December 2021

PACS:	TB96
	TB99

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	CUI Fa-yi

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CUI Fa-yi. Quantum-inspired Enhancement Algorithms of Electron Microscope Images[J]. Acta Metrologica Sinica, 2021, 42(12): 1585-1595.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.12.06 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I12/1585

［1］Manju A, Nigam M J. Applications of quantum inspired computational intelligence: a survey［J］. Artificial Intelligence Review, 2014, 42(1): 79-156.
［2］Eldar Y C, Oppenheim A V. Quantum signal processing［J］. IEEE Signal Processing Magazine, 2002, 19(6): 12-32.
［3］谢可夫, 罗安. 量子启发数学形态学的研究［J］. 电子学报, 2005, 33(2): 284-287.
Xie K F, Luo A. Research on quantum-inspired mathematical morphology［J］. Acta Electronica Sinica, 2005, 33(2): 284-287.
［4］谢可夫, 周心一, 许光平. 量子衍生坍缩形态学滤波［J］. 中国图象图形学报, 2009, 14(5): 967-972.
Xie K F, Zhou X Y, Xu G P. Morphology filtering inspired by quantum collapsing［J］. Journal of Image and Graphics, 2009, 14(5): 967-972.
［5］谢可夫. 量子衍生图像处理方法的研究［D］. 长沙: 中南大学, 2007.
［6］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.
［7］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.
［8］付晓薇, 代芸, 陈黎, 等. 基于局部熵的量子衍生医学超声图像去斑［J］. 电子与信息学报, 2015, 37(3): 560-566.
Fu X W, Dai Y, Chen L, et al. Quantum-inspired despeckling of medical ultrasound images based on local entropy［J］. Journal of Electronics & Information Technology, 2015, 37(3): 560-566.
［9］付晓薇, 王奕, 陈黎, 等. 量子衍生PDE医学超声图像去斑［J］. 中国图象图形学报, 2015, 20(1): 125-131.
Fu X W, Wang Y, Chen L, et al. Quantum-inspired partial differential equation-based medical ultrasound image despeckling method［J］. Journal of Image and Graphics, 2015, 20(1): 125-131.
［10］付晓薇. 基于量子力学的图像处理方法研究［D］. 武汉: 华中科技大学, 2010.
［11］高静波, 吴成茂, 田小平. 基于清晰度的彩色图像量子增强算法［J］. 西安邮电学院学报, 2011, 16(2): 4-8, 25.
Gao J B, Wu C M, Tian X P. Quantum enhancement algorithms of colour image based on digital image definition［J］. Journal of Xian University of Posts and Telecommunications, 2011, 16(2): 4-8, 25.
［12］毕思文, 柯余仙. 量子遥感图像数据增强算法研究［J］. 全球变化数据学报, 2018, 2(4): 367-376.
Bi S W, Ke Y X. Enhancement algorithm for quantum remote sensing image data［J］. Journal of Global Change Data & Discovery, 2018, 2(4): 367-376.
［13］牛培峰, 王丘亚, 马云鹏, 等. 基于量子自适应鸟群算法的锅炉NOx排放特性研究［J］. 计量学报, 2017, 38(6): 770-775.
NIU P F. Wang Q Y, Ma Y P, et al. Study on NOx emission from boiler based on quantum adaptation bird swarm algorithm［J］. Acta Metrologica Sinica, 2017, 38(6): 770-775.
［14］Guo Y, Wei L S, Xu X. A sonar image segmentation algorithm based on quantum-inspired particle swarm optimization and fuzzy clustering［J］. Neural Computing & Applications, 2020, 32(22): 16775-16782.
［15］Tu S S, Rehman O U, Rehman S U, et al. A novel quantum inspired particle swarm optimization algorithm for electromagnetic applications［J］. IEEE Access, 2020, 8: 21909-21916.
［16］张沫,郑慧峰,倪豪,等. 基于遗传算法优化支持向量机的超声图像缺陷分类［J］. 计量学报, 2019, 40(5): 887-892.
Zhang M, Zheng H F, Ni H, et al. Ultrasonic Image Defect Classification Based on Support Vector Machine Optimized by Genetic Algorithm［J］. Acta Metrologica Sinica, 2019, 40(5): 887-892.
［17］Suo J W, Gu L Z, Pan Y, et al. Quantum inspired genetic algorithm for double digest problem［J］. IEEE Access, 2020, 8: 72910-72916.
［18］Arrazola J M, Delgado A, Bardhan B R, et al. Quantum-inspired algorithms in practice［J］. Quantum, 2020, 4: 307.
［19］Zhang Y Z, Song D W, Zhang P, et al. A quantum-inspired sentiment representation model for twitter sentiment analysis［J］. Applied Intelligence, 2019, 49(8): 3093-3108.
［20］Li Q C, Gkoumas D, Lioma C. Quantum-inspired multimodal fusion for video sentiment analysis［J］. Information Fusion, 2021, 65: 58-71.
［21］Biamonte J, Wittek P, Pancotti N, et al. Quantum machine learning［J］. Nature, 2017, 549: 195-202.
［22］Tiwari P, Melucci M. Towards a quantum-inspired binary classifier［J］. IEEE Access, 2019, 7: 42354-42372.
［23］Ding C, Bao T Y, Huang H L. Quantum-inspired support vector machine［J］. Journal of Latex Class Files, 2020, 14(8): 1-12.
［24］Gao Z H, Zhang Y, Zhou S S, et al. An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum［J］. Textile Research Journal, 2021, 91(11-12): 1211-1226.
［25］张永卓,王晶,傅博强,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.
［26］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.
［27］周激流, 吕航. 一种基于新型遗传算法的图像自适应增强算法的研究［J］. 计算机学报, 2001, 24(9): 959-964.
Zhou J L, LÜ H. Image enhancement based on a new genetic algorithm［J］. Chinese Journal of Computers, 2001, 24(9): 959-964.
［28］Maschal R, Young S S. Locally adaptive contrast enhancement and dynamic range compression［C］//SPIE Proceedings, 2012, 8355: 8355181-83551810.