Infrared and Laser Engineering, Volume. 52, Issue 4, 20220461(2023)
System design of multi-resolution microscopic correlation imaging based on deep learning
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Fig. 2. Structure design of multi-resolution microscopic correlation imaging system
Fig. 4. Imaging results of multi-resolution microscopic correlated imaging for highest resolution target
Fig. 5. Comparison of imaging results of biological samples from GI and DLGI with different sampling rates
Fig. 6. Comparison of imaging results of biological samples from GI and DLGI with different sampling rates
Fig. 7. Imaging results of resolution target. (a) GI；(b) DLGI；(c) Ground truth; (d)-(f) Normalized intensity distribution of target position in image; (g) The table represents the line spacing corresponding to different positions of resolution target (Newport)
Table 1. Time and memory consumption of images with different sampling rates
View tableView in ArticleTable 1. Time and memory consumption of images with different sampling rates
Sampling rate 100% 90% 80% 70% 60% 50% Sampling time/s 0.745 0.671 0.596 0.522 0.447 0.373 DL time/s 0.051 0.051 0.051 0.051 0.051 0.051 Total imaging time/s 0.796 0.722 0.647 0.573 0.498 0.424 Memory consumption/kB 1 048 704 943 833 838 963 734 092 629 222 524 352
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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
Paper Information
Category: Optical imaging
Received: Jul. 5, 2022
Accepted: --
Published Online: Jul. 4, 2023
The Author Email: Feng Yueshu (FengYS2020@163.com)
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