[1] Haiyang YU, Huiying ZHANG, Simeng LIU et al. Research of fiber braking in the single fiber scanning endoscopic imaging system. Acta Photonica Sinica, 50, 0511003(2021).

[2] A SHINDE, M V MATHAM. Pixelate removal in an image fiber probe endoscope incorporating comb structure removal methods. Journal of Medical Imaging and Health Informatics, 4, 203-211(2014).

[3] J H HAN, S M YOON. Depixelation of coherent fiber bundle endoscopy based on learning patterns of image prior. Optics Letters, 36, 3212-3214(2011).

[4] G W CHEON, J CHA, J U KANG. Random transverse motion-induced spatial compounding for fiber bundle imaging. Optics Letters, 39, 4368-4371(2014).

[5] J SHAO, W LIAO, R LIANG et al. Resolution enhancement for fiber bundle imaging using maximum a posteriori estimation. Optics Letters, 43, 1906-1909(2018).

[6] J K MIN, M S KWAK, J M CHA. Overview of deep learning in gastrointestinal endoscopy. Gut and Liver, 13, 388(2019).

[7] P SHARMA, A PANTE, S A GROSS. Artificial intelligence in endoscopy. Gastrointestinal Endoscopy, 91, 925-931(2020).

[8] T ITOH, H KAWAHIRA, H NAKASHIMA et al. Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images. Endoscopy International Open, 6, E139-E144(2018).

[9] S SHICHIJO, S NOMURA, K AOYAMA et al. Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBio Medicine, 25, 106-111(2017).

[10] T HIRASAWA, K AOYAMA, T TANIMOTO et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer, 21, 653-660(2018).

[11] M F BYRNE, N CHAPADOS, F SOUDAN et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut, 68, 94-100(2019).

[12] O RONNEBERGER, P FISCHER, T BROX. U-net: Convolutional networks for biomedical image segmentation, 234-241(2015).

[13] I GOODFELLOW, J POUGET-ABADIE, M MIRZA et al. Generative adversarial networks. Communications of the ACM, 63, 139-144(2020).

[14] M WEIGERT, U SCHMIDT, T BOOTHE et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nature Methods, 15, 1090-1097(2018).

[15] X QIN, Z ZHANG, C HUANG et al. U2-Net: Going deeper with nested U-structure for salient object detection. Pattern Recognition, 106, 107404(2020).

[16] Huijuan TIAN, Jiahao ZHAI, Jianxin LIU et al. A low-quality iris image segmentation algorithm based on SRN-UNet. Acta Photonica Sinica, 51, 0210006(2022).

[17] S H KIM, J KIM, S YANG et al. Automatic and quantitative measurement of alveolar bone level in OCT images using deep learning. Biomedical Optics Express, 13, 5468-5482(2022).

[18] T LIU, Y LU, Y ZHANG et al. A bone segmentation method based on Multi-scale features fuse U2Net and improved dice loss in CT image process. Biomedical Signal Processing and Control, 77, 103813(2022).

[19] O OKTAY, J SCHLEMPER, L L FOLGOC et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint(2018).

[20] C GUO, M SZEMENVEI, Y YI et al. Sa-unet: Spatial attention u-net for retinal vessel segmentation, 1236-1242(2021).

[21] B WANG, S QIU, H HE. Dual encoding u-net for retinal vessel segmentation, 84-92(2019).

[22] S LIAN, Z LUO, Z ZHONG et al. Attention guided U-Net for accurate iris segmentation. Journal of Visual Communication and Image Representation, 56, 296-304(2018).

[23] J WANG, X LIU. Medical image recognition and segmentation of pathological slices of gastric cancer based on Deeplab v3+ neural network. Computer Methods and Programs in Biomedicine, 207, 106210(2021).

[24] D RAVI, A B SZCZOTKA, S P PEREIRA et al. Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy. Medical Image Analysis, 53, 123-131(2019).

[25] J SHAO, J ZHANG, R LIANG et al. Fiber bundle imaging resolution enhancement using deep learning. Optics Express, 27, 15880-15890(2019).

[26] J SHAO, J ZHANG, X HUANG et al. Fiber bundle image restoration using deep learning. Optics Letters, 44, 1080-1083(2019).

[27] L C CHEN, Y ZHU, G PAPANDREOU et al. Encoder-decoder with atrous separable convolution for semantic image segmentation, 801-818(2018).

[28] Q HUYNH-THU, M GHANBARI. Scope of validity of PSNR in image/video quality assessment. Electronics Letters, 44, 800-801(2008).

[29] Z WANG, A C BOVIK, H R SHEIKH et al. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600-612(2004).