[1] Boppart, S. A., Deutsch, T. F. & Rattner, D. W. Optical imaging technology in minimally invasive surgery: current status and future directions. Surgical Endoscopy 13, 718-722 (1999). doi: 10.1007/s004649901081
[2] Gulati, S. et al. The future of endoscopy: Advances in endoscopic image innovations. Digestive Endoscopy 32, 512-522 (2020). doi: 10.1111/den.13481
[3] Zhi, Z. W. et al. Supercontinuum light source enables in vivo optical microangiography of capillary vessels within tissue beds. Optics Letters 36, 3169-3171 (2011). doi: 10.1364/OL.36.003169
[4] Park, C. H. et al. Role of probe-based confocal laser endomicroscopy-targeted biopsy in the molecular and histopathological study of gastric cancer. Journal of Gastroenterology and Hepatology 34, 84-91 (2019). doi: 10.1111/jgh.14471
[5] Bouma, B. E. et al. Evaluation of intracoronary stenting by intravascular optical coherence tomography. Heart 89, 317-320 (2003). doi: 10.1136/heart.89.3.317
[6] Kaur, M., Lane, P. M. & Menon, C. Endoscopic optical imaging technologies and devices for medical purposes: state of the art. Applied Sciences 10, 6865 (2020). doi: 10.3390/app10196865
[7] Kim, Y. et al. Smartphone-based rigid endoscopy device with hemodynamic response imaging and laser speckle contrast imaging. Biosensors 13, 816 (2023). doi: 10.3390/bios13080816
[8] Yuan, W. et al. Theranostic oct microneedle for fast ultrahigh-resolution deep-brain imaging and efficient laser ablation in vivo. Science Advances 6, eaaz9664 (2020). doi: 10.1126/sciadv.aaz9664
[9] Zhang, Q. et al. Diffractive optical elements 75 years on: from micro-optics to metasurfaces. Photonics Insights 2, R09 (2023). doi: 10.3788/PI.2023.R09
[10] Gissibl, T. et al. Sub-micrometre accurate free-form optics by three-dimensional printing on single-mode fibres. Nature Communications 7, 11763 (2016). doi: 10.1038/ncomms11763
[11] Ren, H. R. et al. An achromatic metafiber for focusing and imaging across the entire telecommunication range. Nature Communications 13, 4183 (2022). doi: 10.1038/s41467-022-31902-3
[12] Vasquez-Lopez, S. A. et al. Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber. Light: Science & Applications 7, 110 (2018). doi: 10.1038/s41377-018-0111-0
[13] Turtaev, S. et al. High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging. Light: Science & Applications 7, 92 (2018). doi: 10.1038/s41377-018-0094-x
[14] Li, S. H. et al. Memory effect assisted imaging through multimode optical fibres. Nature Communications 12, 3751 (2021). doi: 10.1038/s41467-021-23729-1
[15] Cifuentes, A. et al. Polarization-resolved secondharmonic generation imaging through a multimode fiber. Optica 8, 1065-1074 (2021). doi: 10.1364/OPTICA.430295
[16] Stellinga, D. et al. Time-of-flight 3D imaging through multimode optical fibers. Science 374, 1395-1399 (2021). doi: 10.1126/science.abl3771
[17] Wen, Z. et al. Single multimode fibre for in vivo lightfield-encoded endoscopic imaging. Nature Photonics 17, 679-687 (2023). doi: 10.1038/s41566-023-01240-x
[18] Zhan, N. et al. Enhanced ultrafine multimode fiber imaging based on mode modulation through singular value decomposition. Photonics Research 12, 2214-2225 (2024). doi: 10.1364/PRJ.529353
[19] Yu, H. Y. et al. All-optical image transportation through a multimode fibre using a miniaturized diffractive neural network on the distal facet. Nature Photonics 19, 486-493 (2025). doi: 10.1038/s41566-025-01621-4
[20] Liu, N. H. et al. Rotational memory effect-inspired radon domain learning empowers image transmission through multimode fibers. Laser & Photonics Reviews 19, e00089 (2025). doi: 10.1002/lpor.202500089
[21] Zhou, Y. B. et al. Multiplexing-enhanced computational imaging: High-fidelity reconstruction and color imaging in multimode fibers. Laser & Photonics Reviews 20, e01267 (2026).
[22] Du, Y. et al. Hybrid multimode-multicore fibre based holographic endoscope for deep-tissue neurophotonics. Light: Advanced Manufacturing 3, 408-416 (2022).
[23] Zolnacz, K. et al. Multicore fiber with thermally expanded cores for increased collection efficiency in endoscopic imaging. Light: Advanced Manufacturing 5, 580-587 (2024). doi: 10.37188/lam.2024.049
[24] Shanker, A. et al. Quantitative phase imaging endoscopy with a metalens. Light: Science & Applications 13, 305 (2024). doi: 10.1038/s41377-024-01587-y
[25] Li, H. et al. 500 μm field-of-view probe-based confocal microendoscope for large-area visualization in the gastrointestinal tract. Photonics Research 9, 1829-1841 (2021). doi: 10.1364/PRJ.431767
[26] Froch, J. E. et al. Real time full-color imaging in a ¨ Meta-optical fiber endoscope. eLight 3, 13 (2023). doi: 10.1186/s43593-023-00044-4
[27] Lich, J. et al. Single-shot 3D incoherent imaging with diffuser endoscopy. Light: Advanced Manufacturing 5, 218-228 (2024). doi: 10.37188/lam.2024.015
[28] Skarsoulis, K. et al. Ptychographic imaging with a fiber endoscope via wavelength scanning. Optica 11, 782-790 (2024). doi: 10.1364/OPTICA.519965
[29] Wu, J. C. et al. Single-shot lensless imaging with fresnel zone aperture and incoherent illumination. Light: Science & Applications 9, 53 (2020). doi: 10.1038/s41377-020-0289-9
[30] Huang, Z. Z. & Cao, L. C. Quantitative phase imaging based on holography: trends and new perspectives. Light: Science & Applications 13, 145 (2024). doi: 10.1038/s41377-024-01453-x
[31] Gao, Y. H. & Cao, L. C. Model-based deep learning enables time-resolved computational microscopy. PhotoniX 7, 3 (2026). doi: 10.1186/s43074-025-00222-2
[32] Tsvirkun, V. et al. Flexible lensless endoscope with a conformationally invariant multi-core fiber. Optica 6, 1185-1189 (2019). doi: 10.1364/OPTICA.6.001185
[33] Kuschmierz, R. et al. Ultra-thin 3D lensless fiber endoscopy using diffractive optical elements and deep neural networks. Light: Advanced Manufacturing 2, 415-424 (2021). doi: 10.37188/lam.2021.030
[34] Wu, J. C. et al. Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis. Scientific Reports 12, 18846 (2022). doi: 10.1038/s41598-022-23490-5
[35] Sun, J. W. et al. Quantitative phase imaging through an ultra-thin lensless fiber endoscope. Light: Science & Applications 11, 204 (2022). doi: 10.1038/s41377-022-00898-2
[36] Badt, N. & Katz, O. Real-time holographic lensless micro-endoscopy through flexible fibers via fiber bundle distal holography. Nature Communications 13, 6055 (2022). doi: 10.1038/s41467-022-33462-y
[37] Stephan, R. et al. Bendable fiber lens for minimally invasive endoscopy. Laser & Photonics Reviews 19, 2401757 (2025). doi: 10.1002/lpor.202401757
[38] Sun, J. W. et al. Lensless fiber endomicroscopy in biomedicine. PhotoniX 5, 18 (2024). doi: 10.1186/s43074-024-00133-8
[39] Chen, Z. Q. et al. Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology. Advanced Imaging 2, 041003 (2025). doi: 10.3788/AI.2025.10010
[40] Dremel, J. et al. Lensless single-shot multicore fiber endomicroscopy using a single multispectral hologram. Light: Advanced Manufacturing 6, 896-903 (2025). doi: 10.37188/lam.2025.027
[41] Lee, C. Y. & Han, J. H. Integrated spatio-spectral method for efficiently suppressing honeycomb pattern artifact in imaging fiber bundle microscopy. Optics Communications 306, 67-73 (2013). doi: 10.1016/j.optcom.2013.05.045
[42] Lee, C. Y. & Han, J. H. Elimination of honeycomb patterns in fiber bundle imaging by a superimposition method. Optics Letters 38, 2023-2025 (2013). doi: 10.1364/OL.38.002023
[43] Shao, J. B. et al. Resolution enhancement for fiber bundle imaging using maximum a posteriori estimation. Optics Letters 43, 1906-1909 (2018). doi: 10.1364/OL.43.001906
[44] Wang, J. Y. et al. Honeycomb effect elimination in differential phase fiber-bundle-based endoscopy. Optics Express 32, 20682-20694 (2024). doi: 10.1364/OE.526033
[45] Tsvirkun, V. et al. Widefield lensless endoscopy with a multicore fiber. Optics Letters 41, 4771-4774 (2016). doi: 10.1364/OL.41.004771
[46] Andresen, E. R. et al. Measurement and compensation of residual group delay in a multi-core fiber for lensless endoscopy. Journal of the Optical Society of America B 32, 1221-1228 (2015). doi: 10.1364/JOSAB.32.001221
[47] Kim, Y. et al. Semi-random multicore fibre design for adaptive multiphoton endoscopy. Optics Express 26, 3661-3673 (2018). doi: 10.1364/OE.26.003661
[48] Zhao, J. et al. High-fidelity imaging through multimode fibers via deep learning. Journal of Physics: Photonics 3, 015003 (2021). doi: 10.1088/2515-7647/abcd85
[49] Resisi, S., Popoff, S. M. & Bromberg, Y. Image transmission through a dynamically perturbed multimode fiber by deep learning. Laser & Photonics Reviews 15, 2000553 (2021). doi: 10.1002/lpor.202000553
[50] Liu, Z. T. et al. All-fiber high-speed image detection enabled by deep learning. Nature Communications 13, 1433 (2022). doi: 10.1038/s41467-022-29178-8
[51] Hu, X. W. et al. Unsupervised full-color cellular image reconstruction through disordered optical fiber. Light: Science & Applications 12, 125 (2023). doi: 10.1038/s41377-023-01183-6
[52] Abdulaziz, A. et al. Robust real-time imaging through flexible multimode fibers. Scientific Reports 13, 11371 (2023). doi: 10.1038/s41598-023-38480-4
[53] Feng, H. G., Zhu, R. Z. & Xu, F. Feature-enhanced fiber bundle imaging based on light field acquisition. Advanced Imaging 1, 011002 (2024). doi: 10.3788/AI.2024.10002
[54] Sun, J. W. et al. Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-toend deep learning. Optics Letters 49, 342-345 (2024). doi: 10.1364/OL.509772
[55] Wang, F., Czarske, J. W. & Situ, G. Deep learning for computational imaging: from data-driven to physicsenhanced approaches. Advanced Photonics 7, 054002 (2025).
[56] Sun, J. W. et al. AI-driven projection tomography with multicore fibre-optic cell rotation. Nature Communications 15, 147 (2024). doi: 10.1038/s41467-023-44280-1
[57] Wang, T. J. et al. Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis. Neurophotonics 11, S11505 (2024).
[58] Shao, J. B. et al. Fiber bundle image restoration using deep learning. Optics Letters 44, 1080-1083 (2019). doi: 10.1364/OL.44.001080
[59] Kim, E. et al. Honeycomb artifact removal using convolutional neural network for fiber bundle imaging. Sensors 23, 333 (2023).
[60] Chen, J. L., Shang, W. F. & Xu, S. Endoir: A GAN-based method for fiber bundle endoscope image restoration. Optics and Lasers in Engineering 184, 108588 (2025). doi: 10.1016/j.optlaseng.2024.108588
[61] Renteria, C. et al. Depixelation and enhancement of fiber bundle images by bundle rotation. Applied Optics 59, 536-544 (2020). doi: 10.1364/AO.59.000536
[62] Chen, L. Y. et al. Simple baselines for image restoration. In Computer Vision–ECCV 2022 (eds Avidan, S. et al.) 17–33 (Springer Nature Switzerland, Cham, 2022).
[63] LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436-444 (2015). doi: 10.1038/nature14539
[64] Guay, P. et al. Correcting photodetector nonlinearity in dual-comb interferometry. Optics Express 29, 29165-29174 (2021). doi: 10.1364/OE.435701
[65] Russakovsky, O. et al. ImageNet large scale visual recognition challenge. International Journal of Computer Vision 115, 211-252 (2015). doi: 10.1007/s11263-015-0816-y
[66] Wang, K. Q. & Lam, E. Y. Deep learning phase recovery: Data-driven, physics-driven, or a combination of both?. Advanced Photonics Nexus 3, 056006 (2024). doi: 10.1117/1.apn.3.5.056006
[67] Shen, Y. X. et al. Comparative study of the influence of imaging resolution on linear retardance parameters derived from the Mueller matrix. Biomedical Optics Express 12, 211-225 (2020).
[68] Pshenay-Severin, E. et al. Multimodal nonlinear endomicroscopic imaging probe using a double-core double-clad fiber and focus-combining micro-optical concept. Light: Science & Applications 10, 207 (2021). doi: 10.1038/s41377-021-00648-w
[69] Schmidt, K. et al. Chromatic aberration correction employing reinforcement learning. Optics Express 31, 16133-16147 (2023). doi: 10.1364/OE.487045