[1] Balasubramanian, H. et al. Imagining the future of optical microscopy: everything, everywhere, all at once. Communications Biology 6, 1096 (2023). doi: 10.1038/s42003-023-05468-9
[2] Davidson, M. W. & Abramowitz, M. Optical microscopy. in Encyclopedia of Imaging Science and Technology (ed Hornak, J. P.) (Hoboken: John Wiley & Sons, 2002), 120.
[3] Ma, L. & Fei, B. W. Comprehensive review of surgical microscopes: technology development and medical applications. Journal of Biomedical Optics 26, 010901 (2021).
[4] Sijtsema, N. M. et al. Confocal direct imaging Raman microscope: design and applications in biology. Applied Spectroscopy 52, 348-355 (1998). doi: 10.1366/0003702981943806
[5] Del Rosario, M. et al. The field guide to 3D printing in optical microscopy for life sciences. Advanced Biology 6, 2100994 (2022). doi: 10.1002/adbi.202100994
[6] Ghosh, B. & Agarwal, K. Viewing life without labels under optical microscopes. Communications Biology 6, 559 (2023). doi: 10.1038/s42003-023-04934-8
[7] Chen, X. D., Zheng, B. & Liu, H. Optical and digital microscopic imaging techniques and applications in pathology. Analytical Cellular Pathology 34, 5-18 (2011). doi: 10.1155/2011/150563
[8] Pallen, S. et al. Advances in nonlinear optical microscopy techniques for in vivo and in vitro neuroimaging. Biophysical Reviews 13, 1199-1217 (2021). doi: 10.1007/s12551-021-00832-7
[9] Ma, J. H. et al. Review of wafer surface defect detection methods. Electronics 12, 1787 (2023). doi: 10.3390/electronics12081787
[10] Chen, L. W. et al. Microsphere—toward future of optical microscopes. iScience 23, 101211 (2020). doi: 10.1016/j.isci.2020.101211
[11] Li, X. T. et al. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artificial Intelligence Review 55, 4809-4878 (2022). doi: 10.1007/s10462-021-10121-0
[12] Zhang, Y. F. et al. From whole slide imaging to microscopy: deep microscopy adaptation network for histopathology cancer image classification. in 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (eds Shen, D. G. et al.) (Shenzhen: Springer, 2019), 360-368.
[13] Guo, C. F. et al. OpenWSI: a low-cost, high-throughput whole slide imaging system via single-frame autofocusing and open-source hardware. Optics Letters 45, 260-263 (2020). doi: 10.1364/OL.45.000260
[14] Zhang, X. B. et al. A novel auto-focus method for image processing using laser triangulation. IEEE Access 7, 64837-64843 (2019). doi: 10.1109/ACCESS.2019.2914186
[15] Bian, Z. C. et al. Autofocusing technologies for whole slide imaging and automated microscopy. Journal of Biophotonics 13, e202000227 (2020). doi: 10.1002/jbio.202000227
[16] Han, C. H. L. et al. A novel coaxial focus position detection technique based on differential modulation evaluation for laser direct photolithography. Optics and Lasers in Engineering 161, 107396 (2023). doi: 10.1016/j.optlaseng.2022.107396
[17] Jiang, R. L. et al. Large dynamic range of secondary position errors detection for the coaxial three-mirror system using dual-branch convolutional neural network. Proceedings of SPIE 13154, Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Detector Technologies. Nanjing, China: SPIE, 2024, 1315407.
[18] Silfies, J. et al. The Nikon perfect focus system (PFS). at https://www.microscopyu.com/tutorials/the-nikon-perfect-focus-system-pfs URL.
[19] Chan, C. C. & Chen, H. H. Improving the reliability of phase detection autofocus. Proceedings of the 14th Photography, Mobile, and Immersive Imaging 2018. Burlingame, CA, USA: IS&T, 2018, 1-5.
[20] Huang, H. Y. et al. Deformable motion compensation in interventional cone-beam CT with a context-aware learned autofocus metric. Medical Physics 51, 4158-4180 (2024). doi: 10.1002/mp.17125
[21] Chinnasamy, S. et al. A review on hill climbing optimization methodology. Recent Trends in Management and Commerce 3, 1-7 (2022). doi: 10.46632/rmc/3/1/1
[22] Pinkard, H. et al. Deep learning for single-shot autofocus microscopy. Optica 6, 794-797 (2019). doi: 10.1364/OPTICA.6.000794
[23] Subbarao, M. & Tyan, J. Optimal focus measure for passive autofocusing and depth-from-focus. Proceedings of SPIE 2598, Videometrics IV. Philadelphia, PA, USA: SPIE, 1995, 89-99.
[24] Langehanenberg, P. et al. Automated three-dimensional tracking of living cells by digital holographic microscopy. Journal of Biomedical Optics 14, 014018 (2009). doi: 10.1117/1.3080133
[25] Jiang, S. W. et al. Transform-and multi-domain deep learning for single-frame rapid autofocusing in whole slide imaging. Biomedical Optics Express 9, 1601-1612 (2018). doi: 10.1364/BOE.9.001601
[26] Dastidar, T. R. & Ethirajan, R. Whole slide imaging system using deep learning-based automated focusing. Biomedical Optics Express 11, 480-491 (2020). doi: 10.1364/BOE.379780
[27] Liao, J. et al. Deep learning-based single-shot autofocus method for digital microscopy. Biomedical Optics Express 13, 314-327 (2022). doi: 10.1364/BOE.446928
[28] Xin, K. F. et al. Low-cost whole slide imaging system with single-shot autofocusing based on color-multiplexed illumination and deep learning. Biomedical Optics Express 12, 5644-5657 (2021). doi: 10.1364/BOE.428655
[29] Li, Q. et al. Learning to autofocus in whole slide imaging via physics-guided deep cascade networks. Optics Express 30, 14319-14340 (2022). doi: 10.1364/OE.416824
[30] Gu, Y. Single-shot focus estimation for microscopy imaging with kernel distillation. IEEE Transactions on Computational Imaging 9, 542-550 (2023). doi: 10.1109/TCI.2023.3281189
[31] Gibson, S. F. & Lanni, F. Diffraction by a circular aperture as a model for three-dimensional optical microscopy. Journal of the Optical Society of America A 6, 1357-1367 (1989). doi: 10.1364/JOSAA.6.001357
[32] Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nature Methods 9, 676-682 (2012). doi: 10.1038/nmeth.2019
[33] Kirshner, H., Sage, D. & Unser, M. 3D PSF models for fluorescence microscopy in ImageJ. Proceedings of the Twelfth International Conference on Methods and Applications of Fluorescence Spectroscopy, Imaging and Probes (MAF’11). Strasbourg, French Republic: MAF, 2011, 154.
[34] Howard, A. et al. Searching for MobileNetv3. Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE, 2019, 1314-1324.
[35] Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE, 2015, 3431-3440.