Jinjing Wu1,*, Yang Yuan2,*, 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.