Wireless Capsule Endoscope Low-light Image Enhancement with Balanced Brightness and Saturation

被引:0
|
作者
Li, Wenzhuo [1 ]
Wang, Yinghui [1 ,2 ]
Li, Wei [1 ]
Huang, Liangyi [3 ]
Shukurov, Kamoliddin [4 ]
Wang, Mingfeng [5 ]
机构
[1] Jiangnan Univ, Sch Artificial Intelligence & Comp Sci, Wuxi, Jiangsu, Peoples R China
[2] Minist Educ, Engn Res Ctr Intelligent Technol Healthcare, Wuxi, Jiangsu, Peoples R China
[3] Arizona State Univ, Sch Comp & Augmented Intelligence, Tempe, AZ USA
[4] Tashkent Univ Informat Technol, Dept Artificial Intelligence, Tashkent, Uzbekistan
[5] Brunel Univ London, Dept Mech & Aerosp Engn, London, England
基金
中国国家自然科学基金;
关键词
Wireless capsule endoscope; Low-light image enhancement; HSV color model; CONTRAST ENHANCEMENT; TRANSFORMATION; RETINEX;
D O I
10.1145/3652583.3658034
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
An image enhancement method, which is solving the issue of detail loss caused by the inability of existing image enhancement methods to balance brightness and saturation in Wireless Capsule Endoscope (WCE) low-light environment, is proposed. Firstly, we design a multi-scale fast guided filter to estimate the illumination component and utilize the OTSU method to determine the function parameters based on the grayscale information of the illumination component. Secondly, we construct a brightness enhancement function based on the Weber-Fechner law to achieve brightness enhancement of the V component image. At the same time, we designed the brightness enhancement coefficient and combined with Haar wavelet to operate the S component image to balance the brightness and saturation of the WCE enhanced image. Finally, the image enhancement result is obtained by merging the channels and converting to the RGB color space. Comprehensive experimental results show that compared with existing methods, our proposed method improves the mean, standard deviation and information entropy evaluation criteria by 18.2, 5.81 and 0.26 respectively. Furthermore, the feature point detection and matching numbers of the enhanced images increased by an average of 59.3% and 32.9% respectively. Moreover, the effectiveness of this method is further verified through the improvement of experimental results of single-image depth estimation accuracy.
引用
收藏
页码:998 / 1005
页数:8
相关论文
共 50 条
  • [41] Low-light image enhancement by diffusion pyramid with residuals
    Kim, Wonjun
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2021, 81
  • [42] Retinex-based Low-Light Image Enhancement
    Luo, Rui
    Feng, Yan
    He, Mingxin
    Zhang, Yuliang
    2023 ASIA PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE, APSIPA ASC, 2023, : 1429 - 1434
  • [43] Low-light color image enhancement based on NSST
    Wu Xiaochu
    Tang Guijin
    Liu Xiaohua
    Cui Ziguan
    Luo Suhuai
    The Journal of China Universities of Posts and Telecommunications, 2019, (05) : 41 - 48
  • [44] Dual-band low-light image enhancement
    Aizhong Mi
    Wenhui Luo
    Zhanqiang Huo
    Multimedia Systems, 2024, 30
  • [45] Polarization-Aware Low-Light Image Enhancement
    Zhou, Chu
    Teng, Minggui
    Lyu, Youwei
    Li, Si
    Xu, Chao
    Shi, Boxin
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 3, 2023, : 3742 - 3750
  • [46] Low-light image enhancement based on normal-light image degradation
    Bai Zhao
    Xiaolin Gong
    Jian Wang
    Lingchao Zhao
    Signal, Image and Video Processing, 2022, 16 : 1409 - 1416
  • [47] Low-light color image enhancement based on NSST
    Xiaochu W.
    Guijin T.
    Xiaohua L.
    Ziguan C.
    Suhuai L.
    Journal of China Universities of Posts and Telecommunications, 2019, 26 (05): : 41 - 48
  • [48] Low-Light Image Enhancement by Principle Component Analysis
    Priyanka, Steffi Agino
    Wang, Yuan-Kai
    Huang, Shih-Yu
    IEEE ACCESS, 2019, 7 : 3082 - 3092
  • [49] Low-Light Image Enhancement for Multiaperture and Multitap Systems
    Conde, Miguel Heredia
    Zhang, Bo
    Kagawa, Keiichiro
    Loffeld, Otmar
    IEEE PHOTONICS JOURNAL, 2016, 8 (02):
  • [50] A Pipeline Neural Network for Low-Light Image Enhancement
    Guo, Yanhui
    Ke, Xue
    Ma, Jie
    Zhang, Jun
    IEEE ACCESS, 2019, 7 : 13737 - 13744