A Highlight Removal Method for Capsule Endoscopy Images
<|reference_start|>A Highlight Removal Method for Capsule Endoscopy Images: The images captured by Wireless Capsule Endoscopy (WCE) always exhibit specular reflections, and removing highlights while preserving the color and texture in the region remains a challenge. To address this issue, this paper proposes a highlight removal method for capsule endoscopy images. Firstly, the confidence and feature terms of the highlight region's edges are computed, where confidence is obtained by the ratio of known pixels in the RGB space's R channel to the B channel within a window centered on the highlight region's edge pixel, and feature terms are acquired by multiplying the gradient vector of the highlight region's edge pixel with the iso-intensity line. Subsequently, the confidence and feature terms are assigned different weights and summed to obtain the priority of all highlight region's edge pixels, and the pixel with the highest priority is identified. Then, the variance of the highlight region's edge pixels is used to adjust the size of the sample block window, and the best-matching block is searched in the known region based on the RGB color similarity and distance between the sample block and the window centered on the pixel with the highest priority. Finally, the pixels in the best-matching block are copied to the highest priority highlight removal region to achieve the goal of removing the highlight region. Experimental results demonstrate that the proposed method effectively removes highlights from WCE images, with a lower coefficient of variation in the highlight removal region compared to the Crinimisi algorithm and DeepGin method. Additionally, the color and texture in the highlight removal region are similar to those in the surrounding areas, and the texture is continuous.<|reference_end|>
@article{zhang2024a,
    title={A Highlight Removal Method for Capsule Endoscopy Images},
    author={Shaojie Zhang, Yinghui Wang, Peixuan Liu, Wei Li, Jinlong Yang, Tao
  Yan, Yukai Wang, Liangyi Huang, Mingfeng Wang, and Ibragim R. Atadjanov},
    journal={arXiv preprint arXiv:2402.07083},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07083},
    primaryClass={cs.CV}
}