  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.
