Jinjing Wu^1,*, Yang Yuan^2,*, Long Liu¹, Haipo Cui¹, Tianying Xu³, Miao Zhou⁴, Zhanheng Chen³, Bing Xu³

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. ²School of Computer Science and Artificial Intelligence, Changzhou University, Jiangsu 213164, China. ³School of Anesthesiology, Second Military Medical University/Naval Medical University, Shanghai 200433, China. ⁴Department of Anesthesiology, the Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University, Nanjing 210009, Jiangsu, China. 

^*The authors contribute equally.

Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. E-mail: h_b_cui@163.com, Tel: +86-21-55271290; Bing Xu, School of Anesthesiology, Second Military Medical University/Naval Medical University, 800 Xiangyin Road, Shanghai 200433, China. E-mail: mzxubing1992@163.com, Tel: +86-21-81872030; Zhanheng Chen, School of Anesthesiology, Second Military Medical University/Naval Medical University, 800 Xiangyin Road, Shanghai 200433, China. E-mail: chenzhanheng17@mails.ucas.ac.cn, Tel: +86 21 81872034. 

Received July 19, 2023; Accepted September 6, 2023; Published September 30, 2023

DOI: https://doi.org/10.61189/663074tcakcn

Highlights

● Medical endoscopic images can provide doctors with more accurate, visualized, and three-dimensional views of various internal tissues.

● Image processing techniques such as image denoising, image deblurring, image enhancement, and image segmentation can improve the imaging quality of endoscopes.