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Journals >Infrared and Laser Engineering >Volume 52 >Issue 4 >Page 20220461 > Article

Infrared and Laser Engineering
Vol. 52, Issue 4, 20220461 (2023)

System design of multi-resolution microscopic correlation imaging based on deep learning

Yutong Liu¹, Yan Li¹, Lu Jin², Huaxu Tang³..., Shun Wang³, Yucong Wu³ and Yueshu Feng^3,4,*|Show fewer author(s)

Author Affiliations

¹School of Opto-Electronic Engineering, Changchun College of Electronic Technology, Changchun 130114, China

²Research Institute of Environmental Innovation (Suzhou), Tsinghua, Suzhou 215163, China

³Institute for Interdisciplinary Quantum Information Technology, Jilin Engineering Normal University, Changchun 130052, China

⁴Jilin Engineering Laboratory for Quantum Information Technology, Changchun 130052, China

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DOI: 10.3788/IRLA20220461 Cite this Article

Yutong Liu, Yan Li, Lu Jin, Huaxu Tang, Shun Wang, Yucong Wu, Yueshu Feng. System design of multi-resolution microscopic correlation imaging based on deep learning[J]. Infrared and Laser Engineering, 2023, 52(4): 20220461 Copy Citation Text

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References

[1] Wenhui Pan, Bingling Chen, Jianguo Zhang, et al. Compressed sensing STORM super-resolution image reconstruction based on noise correction-principal component analysis preprocessing algorithm. Chinese Journal of Lasers, 47, 0207024(2020).

[2] Saiwen Zhang, Yaqi Deng, Chong Wang, et al. Research on super-resolution fluorescence microscopy imaging based on multiple measurement vector compressed sensing. Infrared and Laser Engineering, 50, 20210484(2021).

[3] Yueshu Feng, Cheng Zhou, Xuan Liu, et al. Study of multi-resolution microscopic correlated imaging based on Hadamard optimized matrix. Acta Optica Sinica, 41, 116-124(2021).

[4] W W Li, Z S Tong, K Xiao, et al. Single-frame wide-field nanoscopy based on ghost imaging via sparsity constraints. Optica, 6, 1515-1522(2019).

[5] H C Liu. Imaging reconstruction comparison of different ghost imaging algorithms. Scientific Reports, 10, 14626(2020).

[6] Zhentao Liu, Chenyu Hu, Zhishen Tong, et al. Some research progress on the theoretical study of ghost imaging in Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (Invited). Infrared and Laser Engineering, 50, 20211059(2021).

[7] J Lee, J Singh. Integration of digital twin and deep learning in cyber-physical systems: Towards smart manufacturing. International Journal of Machine Tools and Manufacture, 38, 901-910(2020).

[8] J D Kumar, Z Zhang, K Q Huang. Multi angle optimal pattern-based deep learning for automatic facial expression recognition-ScienceDirect. Pattern Recognition Letters, 139, 157-165(2020).

[9] B Justine, R Merey, G Silvio, et al. SeedQuant: A deep learning-based tool for assessing stimulant and inhibitor activity on root parasitic seeds. Plant Physiology, 186, 1632-1644(2021).

[10] Hongqiang Zhou, Lingling Huang, Yongtian Wang. Deep learning algorithm and its application in optics. Infrared and Laser Engineering, 48, 1226004(2019).

[11] E Nehme, L E Weiss, T Michaeli, et al. Deep-STORM: Super-resolution single-molecule microscopy by deep learning. Optica, 5, 458-464(2018).

[12] H D Wang, Y Rivenson, Y Y Jin, et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nature Methods, 16, 103-110(2018).

[13] M Lyu, W Wang, H Wang, et al. Deep-learning-based ghost imaging. Scientific Reports, 7, 17865(2017).

[14] F Wang, H Wang, H C Wang, et al. Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging. Optics Express, 27, 25560-25572(2019).

[15] S Rizvi, J Cao, K Zhang, et al. Deringing and denoising in extremely under-sampled fourier single pixel imaging. Optics Express, 28, 7360-7374(2020).

[16] Bhardwaj K, Milosavljevic M, Chalfin A, et al. Collapsible linear blocks f superefficient super resolution[DBOL]. (20210317) [20220826]. https:arxiv.gabs2103.09404v1.

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Yutong Liu, Yan Li, Lu Jin, Huaxu Tang, Shun Wang, Yucong Wu, Yueshu Feng. System design of multi-resolution microscopic correlation imaging based on deep learning[J]. Infrared and Laser Engineering, 2023, 52(4): 20220461

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