[1] A. Perperidis et al. Image computing for fibre-bundle endomicroscopy: a review. Med. Image Anal., 62, 101620(2020).

[2] R. Kuschmierz et al. Ultra-thin 3D lensless fiber endoscopy using diffractive optical elements and deep neural networks. Light Adv. Manuf., 2, 415(2021).

[3] Y. Du et al. Hybrid multimode-multicore fibre based holographic endoscope for deep-tissue neurophotonics. Light Adv. Manuf., 3, 1(2022).

[4] H. Feng et al. Endoscopic displacement measurement based on fiber optic bundles. Opt. Express, 30, 14948(2022).

[5] J. Sun et al. Quantitative phase imaging through an ultra-thin lensless fiber endoscope. Light Sci. Appl., 11, 204(2022).

[6] H. Feng et al. Lensless fiber imaging with long working distance based on active depth measurement. IEEE Trans. Instrum. Meas., 71, 7002507(2022).

[7] KL. Reichenbach, C. Xu. Numerical analysis of light propagation in image fibers or coherent fiber bundles. Opt. Express, 15, 2151(2007).

[8] X. Chen, KL. Reichenbach, C. Xu. Experimental and theoretical analysis of core-to-core coupling on fiber bundle imaging. Opt. Express, 16, 21598(2008).

[9] A. Perperidis et al. Characterization and modelling of inter-core coupling in coherent fiber bundles. Opt. Express, 25, 11932(2017).

[10] J. Han, J. Lee, J. U. Kang. Pixelation effect removal from fiber bundle probe based optical coherence tomography imaging. Opt. Express, 18, 7427(2010).

[11] M. Elter, S. Rupp, C. Winter. Physically motivated reconstruction of fiberscopic images. Proceedings of the International Conference on Pattern Recognition, 599(2006).

[12] P. Wang et al. Fiber pattern removal and image reconstruction method for snapshot mosaic hyperspectral endoscopic images. Biomed Opt. Express, 9, 780(2018).

[13] J. Han, SM. Yoon. Depixelation of coherent fiber bundle endos-copy based on learning patterns of image prior. Opt. Lett., 36, 3212(2011).

[14] J. Shao et al. Resolution enhancement for fiber bundle imaging using maximum a posteriori estimation. Opt. Lett., 43, 1906(2018).

[15] Y. Chang et al. Compact high-resolution endomicroscopy based on fiber bundles and image stitching. Opt. Lett., 43, 4168(2018).

[16] SJ. Miller et al. Targeted detection of murine colonic dysplasia in vivo with flexible multispectral scanning fiber endoscopy. J. Biomed Opt., 17, 021103(2012).

[17] N. Krstajic et al. Two-color widefield fluorescence microendoscopy enables multiplexed molecular imaging in the alveolar space of human lung tissue. J. Biomed Opt., 21, 1(2016).

[18] R. French, S. Gigan. Snapshot fiber spectral imaging using speckle correlations and compressive sensing. Opt. Express, 26, 32302(2018).

[19] M. Plöschner, T. Tyc, T. Čižmár. Seeing through chaos in multi-mode fibres. Nat. Photonics, 9, 529(2015).

[20] E. R. Andresen et al. Toward endoscopes with no distal optics: video-rate scanning microscopy through a fiber bundle. Opt. Lett., 38, 609(2013).

[21] E. Scharf et al. Video-rate lensless endoscope with self-calibration using wavefront shaping. Opt. Lett., 45, 3629(2020).

[22] D. Ravì et al. Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction. Int. J. Comput. Assist. Radiol. Surg., 13, 917(2018).

[23] J. Shao et al. Fiber bundle image restoration using deep learning. Opt. Lett., 44, 1080(2019).

[24] J. Shao et al. Fiber bundle imaging resolution enhancement using deep learning. Opt. Express, 27, 15880(2019).

[25] J. Wu et al. Learned end-to-end high-resolution lensless fiber imaging toward intraoperative real-time cancer diagnosis(2022).

[26] Z. Meng et al. Snapshot multispectral endomicroscopy. Opt. Lett., 45, 3897(2020).

[27] J. P. Dumas et al. Computational endoscopy—a framework for improving spatial resolution in fiber bundle imaging. Opt. Lett., 44, 3968(2019).

[28] J. Shin et al. A minimally invasive lens-free computational microendoscope. Sci. Adv., 5, eaaw5595(2019).

[29] K. Okamoto. Fundamentals of Optical Waveguides(2021).

[30] M. Levoy et al. Light field microscopy. ACM Transact Graph, 25, 924(2006).

[31] A. Orth et al. Extended depth of field imaging through multicore optical fibers. Opt. Express, 26, 6407(2018).

[32] A. Orth et al. Optical fiber bundles: ultra-slim light field imaging probes. Sci. Adv., 5, 1(2019).

[33] P. Isola et al. Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1125(2017).

[34] JY. Zhu et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, 2223(2017).

[35] C. Ledig et al. Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4681(2017).

[36] O. Ronneberger, P. Fischer, T. Brox. U-net: Convolutional net-works for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, 234(2015).

[37] K. He et al. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770(2016).

[38] J. Deng et al. ImageNet: a large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 248(2009).