Runze Zhu, Fei Xu. Multimode Fiber Imaging Based on Temporal-Spatial Information Extraction[J]. Laser & Optoelectronics Progress, 2023, 60(11): 1106011

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- Laser & Optoelectronics Progress
- Vol. 60, Issue 11, 1106011 (2023)

Fig. 1. Research framework of multimode fiber imaging

Fig. 2. Research framework of MMF imaging based on TM measurement

Fig. 3. MMF imaging based on spatial-domain TM measurement. (a) Principle of spatial-domain TM; (b) experimental diagram of spatial-domain TM measurement; (c) experimental diagram of MMF imaging based on spatial-domain TM measurement

Fig. 4. MMF imaging based on frequency-domain TM measurement. (a) Principle of frequency -domain TM; (b) experimental diagram of spatial-domain TM measurement; (c) experimental diagram of MMF imaging based on frequency -domain TM measurement
![Imaging system of deep brain of living mouse based on multimode fiber[31]](/Images/icon/loading.gif)
Fig. 5. Imaging system of deep brain of living mouse based on multimode fiber[31]
![Endoscopic LIDAR[37]. (a) Schematic of experimental setup; (b) snapshot of true scene; (c) typical depth resolved images](/Images/icon/loading.gif)
Fig. 6. Endoscopic LIDAR[37]. (a) Schematic of experimental setup; (b) snapshot of true scene; (c) typical depth resolved images

Fig. 7. Research framework of MMF imaging based on phase conjugation and phase optimization

Fig. 8. MMF imaging based on phase conjugation and phase optimization. (a) Experimental scheme of MMF imaging based on phase conjugation; (b) experimental scheme of MMF imaging based on phase optimization

Fig. 9. Research framework of MMF compressive imaging based on structure illumination

Fig. 10. MMF compressive imaging based on speckle illumination. (a) Principle; (b) experimental scheme
![MMF-based super-resolution and super-speed endo-microscopy[51]. (a) Characterization of imaging resolution and speed using 0.22NA MMF; (b) characterization of imaging resolution and speed using 0.1NA MMF](/Images/icon/loading.gif)
Fig. 11. MMF-based super-resolution and super-speed endo-microscopy[51]. (a) Characterization of imaging resolution and speed using 0.22NA MMF; (b) characterization of imaging resolution and speed using 0.1NA MMF

Fig. 12. Evolution of ultrashort pulses in MMF
![High-speed all-fiber imaging based on temporal information extraction[59]. (a) Schematic of the experimental setup; (b) flow of the reconstruction process; (c)–(e) detailed imaging devices](/Images/icon/loading.gif)
Fig. 13. High-speed all-fiber imaging based on temporal information extraction[59]. (a) Schematic of the experimental setup; (b) flow of the reconstruction process; (c)–(e) detailed imaging devices

Fig. 14. Research framework of machine learning-assisted MMF imaging

Fig. 15. Process of machine learning-assisted multimode fiber imaging
![High-speed all-fiber micro-imaging with large depth of field[71]](/Images/icon/loading.gif)
Fig. 16. High-speed all-fiber micro-imaging with large depth of field[71]

Fig. 17. Research framework of MMF imaging under dynamic perturbance
![Anti-interference imaging based on proximal wavefront measurement. (a) Based on the virtual beacon source[73]; (b) based on the partial reflector[74]; (c) based on the metasurface reflector stacks[75]; (b) based on the guide star[76]](/Images/icon/loading.gif)
Fig. 18. Anti-interference imaging based on proximal wavefront measurement. (a) Based on the virtual beacon source[73]; (b) based on the partial reflector[74]; (c) based on the metasurface reflector stacks[75]; (b) based on the guide star[76]
![Image transmission through a dynamically perturbed multimode fiber by deep learning[82]](/Images/icon/loading.gif)
Fig. 19. Image transmission through a dynamically perturbed multimode fiber by deep learning[82]
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Table 1. Comparison of MMF imaging methods based on spatial-domain information extraction
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Table 2. Parameter comparison of representative works of MMF imaging based on temporal-spatial information extraction

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