Academic Journal
View in EDS
Linked Full Text

Super-Resolution Microscopy for Precision Microsphere Defect Inspection Using Sparrow-Optimized Autocorrelation Deconvolution.

Bibliographic Details
Title: Super-Resolution Microscopy for Precision Microsphere Defect Inspection Using Sparrow-Optimized Autocorrelation Deconvolution.
Authors: He T; Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education, Southwest University of Science and Technology, Mianyang, China., Yu J; Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education, Southwest University of Science and Technology, Mianyang, China., Ou L; Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education, Southwest University of Science and Technology, Mianyang, China., Zheng Q; Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education, Southwest University of Science and Technology, Mianyang, China., Ye J; Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education, Southwest University of Science and Technology, Mianyang, China., Jiang Z; Key Laboratory of Testing Technology for Manufacturing Process in Ministry of Education, Southwest University of Science and Technology, Mianyang, China.
Source: Annals of the New York Academy of Sciences [Ann N Y Acad Sci] 2026 Apr; Vol. 1558 (1), pp. e70263.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: New York Academy of Sciences Country of Publication: United States NLM ID: 7506858 Publication Model: Print Cited Medium: Internet ISSN: 1749-6632 (Electronic) Linking ISSN: 00778923 NLM ISO Abbreviation: Ann N Y Acad Sci Subsets: MEDLINE
Imprint Name(s): Publication: 2006- : New York, NY : Malden, MA : New York Academy of Sciences ; Blackwell
Original Publication: New York, The Academy.
MeSH Terms: Image Processing, Computer-Assisted*/methods , Microscopy*/methods , Microspheres*, Algorithms ; Deep Learning
Abstract: Precision microspheres have small volumes, making the detection of surface defects challenging with the naked eye. Traditional optical microscopy methods are hindered by issues such as localized blurriness and low resolution, which impede their ability to detect surface defects of microspheres with the precision required. This paper proposes an autocorrelation two-step deconvolution super-resolution image reconstruction method using the sparrow search algorithm, which adaptively fine-tunes the acceleration parameters and optimizes them through a hybrid energy function as the objective. This method enhances image resolution and mitigates the occurrence of artifacts during the reconstruction process. Rolling Fourier ring correlation and full width at half maximum are employed to assess the quality of the reconstructed images. In comparison to the traditional autocorrelation two-step deconvolution super-resolution algorithm, the proposed method achieves a resolution enhancement ranging from 2.79 to 3.82 times, depending on the frame count. For equivalent frame counts, the image processing speed increases by 6.3%-34.2%. When compared to various deep learning models, the proposed algorithm reconstructs more detailed features and enhances the detection of surface defects in precision microspheres. It displays a depression effect, which may serve as a valuable reference for the quantitative assessment of surface roughness in precision microsphere images.
(© 2026 The New York Academy of Sciences.)
References: V. Barzdaitis, V. V. Barzdaitis, R. Maskvytis, A. Tadžijevas, and M. Vasylius, “New Deep Groove Ball Bearings High Frequencies Vibration Testing,” Mechanika 20, no. 3 (2014): 287–293, https://doi.org/10.5755/j01.mech.20.3.6740.
R. M. Singleton, B. W. Weinstein, and C. D. Hendricks, “X‐Ray Measurement of Laser Fusion Targets Using Least Squares Fitting,” Applied Optics 18, no. 24 (1979): 4116, https://doi.org/10.1364/AO.18.004116.
R. Cook, G. E. Overturf, S. R. Buckley, and R. McEachern, “Production and Characterization of Doped Mandrels for Inertial‐Confinement Fusion Experiments,” Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 12, no. 4 (1994): 1275–1280, https://doi.org/10.1116/1.579308.
R. Keys, “Cubic Convolution Interpolation for Digital Image Processing,” IEEE Transactions on Acoustics, Speech, and Signal Processing 29, no. 6 (2003): 1153–1160, https://doi.org/10.1109/TASSP.1981.1163711.
J. A. Parker, R. V. Kenyon, and D. E. Troxel, “Comparison of Interpolating Methods for Image Resampling,” IEEE Transactions on Medical Imaging 2, no. 1 (2007): 31–39, https://doi.org/10.1109/TMI.1983.4307610.
M. Jiang and G. Wang, “Development of Iterative Algorithms for Image Reconstruction,” Journal of X‐Ray Science and Technology 10, no. 1–2 (2002): 77–86, https://doi.org/10.3233/XST‐2002‐00059.
K. Jensen and D. Anastassiou, “Subpixel Edge Localization and the Interpolation of Still Images,” IEEE Transactions on Image Processing 4, no. 3 (1995): 285–295, https://doi.org/10.1109/83.366477.
S. P. Kim, N. K. Bose, and H. M. Valenzuela, “Recursive Reconstruction of High Resolution Image From Noisy Undersampled Multiframes,” IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 6 (1990): 1013–1027, https://doi.org/10.1109/83.242363.
A. N. Naser, W. Guiler, Q. Lu, and Y. H. Chen, “Nanoarchitecture and Molecular Interactions of Epithelial Cell Junction Proteins Revealed by Super‐Resolution Microscopy,” Annals of the New York Academy of Sciences 1516, no. 1 (2022): 175–187, https://doi.org/10.1111/nyas.14855.
C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a Deep Convolutional Network for Image Super‐Resolution,” Lecture Notes in Computer Science 8692 (2014): 184–199, https://doi.org/10.1007/978‐3‐319‐10593‐2_13.
C. Dong, C. C. Loy, and X. Tang, “Accelerating the Super‐Resolution Convolutional Neural Network,” Lecture Notes in Computer Science 9906 (2016): 391–407, https://doi.org/10.1007/978‐3‐319‐46475‐6_25.
C. Tian, R. Zhuge, Z. Wu, et al., “Lightweight Image Super‐Resolution With Enhanced CNN,” Knowledge‐Based Systems 205 (2020): 106235, https://doi.org/10.1016/j.knosys.2020.106235.
M. Haris, G. Shakhnarovich, and N. Ukita, “Deep Back‐Projection Networks for Super‐Resolution,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), 1664–1673, https://doi.org/10.1109/CVPR.2018.00179.
J. Yang, J. Wright, T. S. Huang, and Yi Ma, “Image Super‐Resolution via Sparse Representation,” IEEE Transactions on Image Processing 19, no. 11 (2010): 2861–2873, https://doi.org/10.1109/TIP.2010.2050625.
M. A. Alsmirat, E. Qawasmeh, M. Al‐Ayyoub, and Y. Jararweh, “Building an Image Set for Modeling Image Re‐targeting Using Deep Learning,” Simulation Modelling Practice and Theory 126 (2023): 102773, https://doi.org/10.1016/j.simpat.2023.102773.
D. S. Biggs and M. Andrews, “Acceleration of Iterative Image Restoration Algorithms,” Applied Optics 36, no. 8 (1997): 1766, https://doi.org/10.1364/AO.36.001766.
C. J. R. Sheppard, M. Castello, G. Tortarolo, et al., “Pixel Reassignment in Image Scanning Microscopy With a Doughnut Beam: Example of Maximum Likelihood Restoration,” Journal of the Optical Society of America A 38, no. 7 (2021): 1075, https://doi.org/10.1364/JOSAA.426473.
W. Zhao, J. Liu, and H. Li, “Ultrafast Super‐Resolution Imaging via Auto‐Correlation Two‐Step Deconvolution,” Proceedings of SPIE 11497 (2020): 42–49, https://doi.org/10.1117/12.2567853.
M. J. Rust, M. Bates, and X. Zhuang, “Sub‐Diffraction‐Limit Imaging by Stochastic Optical Reconstruction Microscopy (STORM),” Nature Methods 3, no. 10 (2006): 793–796, https://doi.org/10.1038/nmeth929.
E. Betzig, G. H. Patterson, R. Sougrat, et al., “Imaging Intracellular Fluorescent Proteins at Nanometer Resolution,” Science 313, no. 5793 (2006): 1642–1645, https://doi.org/10.1126/science.1127344.
S. T. Hess, T. P. Girirajan, and M. D. Mason, “Ultra‐High Resolution Imaging by Fluorescence Photoactivation Localization Microscopy,” Biophysical Journal 91, no. 11 (2006): 4258–4272, https://doi.org/10.1529/biophysj.106.091116.
A. G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Archives of Computational Methods in Engineering 29, no. 5 (2022): 2531–2561, https://doi.org/10.1007/S11831‐021‐09694‐4.
M. Dorigo, V. Maniezzo, and A. Colorni, “Any System Optimization by a Colony of Cooperating Agents,” IEEE Transactions on System, Man, and Cybernetics, Part B (Cybernetics) 26, no. 1 (1996): 29–41, https://doi.org/10.1109/3477.484436.
W. T. Pan, “A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example,” Knowledge‐Based Systems 26 (2012): 69–74, https://doi.org/10.1016/j.knosys.2011.07.001.
S. Mirjalili, “Dragonfly Algorithm: A New Meta‐Heuristic Optimization Technique for Solving Single‐Objective, Discrete, and Multi‐Objective Problems,” Neural Computing and Applications 27, no. 4 (2016): 1053–1073, https://doi.org/10.1007/s00521‐015‐1920‐1.
A. Askarzadeh, “A Novel Metaheuristic Method for Solving Constrained Engineering Optimization Problems: Crow Search Algorithm,” Computers & Structures 169 (2016): 1–12, https://doi.org/10.1016/j.compstruc.2016.03.001.
J. Xue and B. Shen, “A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm,” Systems Science & Control Engineering 8, no. 1 (2020): 22–34, https://doi.org/10.1080/21642583.2019.1708830.
K. Agarwal and R. Macháň, “Multiple Signal Classification Algorithm for Super‐Resolution Fluorescence Microscopy,” Nature Communications 7, no. 1 (2016): 13752, https://doi.org/10.1038/ncomms13752.
S. Culley, D. Albrecht, C. Jacobs, et al., “Quantitative Mapping and Minimization of Super‐Resolution Optical Imaging Artifacts,” Nature Methods 15, no. 4 (2018): 263–266, https://doi.org/10.1038/nmeth.4605.
W. Zhao, X. Huang, J. Yang, et al., “Quantitatively Mapping Local Quality of Super‐Resolution Microscopy by Rolling Fourier Ring Correlation,” Light: Science & Applications 12, no. 1 (2023): 298, https://doi.org/10.1038/s41377‐023‐01321‐0.
D. Sage, H. Kirshner, T. Pengo, et al., “Quantitative Evaluation of Software Packages for Single‐Molecule Localization Microscopy,” Nature Methods 12, no. 8 (2015): 717–724, https://doi.org/10.1038/nmeth.3442.
M. Heilemann, S. Van De Linde, M. Schüttpelz, et al., “Subdiffraction‐Resolution Fluorescence Imaging With Conventional Fluorescent Probes,” Angewandte Chemie International Edition 47, no. 33 (2008): 6172–6176, https://doi.org/10.1002/anie.200802376.
I. Yahiatene, S. Hennig, M. Müller, and T. Huser, “Entropy‐Based Super‐Resolution Imaging (ESI): From Disorder to Fine Detail,” ACS Photonics 2, no. 8 (2015): 1049–1056, https://doi.org/10.1021/acsphotonics.5b00307.
Grant Information: 52305518 National Natural Science Foundation of China; 52575238 National Natural Science Foundation of China; 2024NSFTTD0019 Sichuan Science and Technology Program
Contributed Indexing: Keywords: autocorrelation two‐step deconvolution; image reconstruction; sparrow algorithm; super‐resolution
Entry Date(s): Date Created: 20260417 Date Completed: 20260715 Latest Revision: 20260715
Update Code: 20260715
DOI: 10.1111/nyas.70263
PMID: 41996601
Database: MEDLINE
Description
ISSN:1749-6632
DOI:10.1111/nyas.70263