An Adaptive Enhancement Method for Gastrointestinal Low-Light Images of Capsule Endoscope
Peixuan Liu, Yinghui Wang, Jinlong Yang, Wei Li
Abstract
Balancing image local detail enhancement with brightness enhancement has been a challenge. The images captured by wireless capsule endoscopy (WCE) are low-light and unclear. To this end, we propose an adaptive enhancement method for WCE images. Firstly, we use the guided filter to filter and smooth the WCE images to approximate its illumination component, and then the reflection component is obtained by decomposing it based on the Retinex model. Secondly, an adaptive Sigmoid function is obtained according to the positive correlation between the just-noticeable difference (JND) threshold of the illumination component and the gain parameter of the Sigmoid function, to adaptively enhance the illumination component, and then based on the Retinex model fusion with the reflection component. Finally, we combine with Gamma correction algorithm to enhance the contrast of the above results. Experimental results show that the proposed method can adaptively enhance the overall effect and local details of WCE images; and the feature extraction and matching effects are better than the classical enhancement algorithms, with an average increase of 88.8% and 59.1% respectively.
BibTeX
@inproceedings{icassp2023_anadaptiveenhanc,
title = {An Adaptive Enhancement Method for Gastrointestinal Low-Light Images of Capsule Endoscope},
author = {Peixuan Liu and Yinghui Wang and Jinlong Yang and Wei Li},
booktitle = {ICASSP 2023},
year = {2023}
}