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
来源
PROCEEDINGS OF THE 4TH ANNUAL ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, ICMR 2024 | 2024年
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
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