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.

翻译：无线胶囊内窥镜(WCE)捕获的图像常存在镜面反射问题，如何在去除高光的同时保留该区域的色彩和纹理仍是一项挑战。针对此问题，本文提出一种胶囊内窥镜图像高光去除方法。首先，计算高光区域边缘像素的置信度和特征项：置信度通过以高光区域边缘像素为中心的窗口内RGB空间R通道与B通道已知像素比值获得；特征项由该边缘像素的梯度向量与等照度线乘积获取。随后，对置信度和特征项赋予不同权重并求和，得到所有高光区域边缘像素的优先级，并确定优先级最高的像素。接着，利用高光区域边缘像素的方差调整样本块窗口尺寸，基于样本块与优先级最高像素中心窗口的RGB颜色相似性和间距，在已知区域搜索最佳匹配块。最后，将最佳匹配块中的像素复制到优先级最高的高光去除区域，实现高光区域去除目标。实验结果表明，所提方法有效去除了WCE图像中的高光，且与Crinimisi算法和DeepGin方法相比，高光去除区域的变异系数更低。同时，高光去除区域的色彩与纹理与周边区域相似，纹理连续性良好。