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：无线胶囊内镜（WCE）采集的图像常存在镜面反射，如何在去除高光的同时保留区域的色彩和纹理仍是一大挑战。针对此问题，本文提出一种胶囊内镜图像高光去除方法。首先，计算高光区域边缘的置信度和特征项，其中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通道已知像素之比获得，特征项通过将高光区域边缘像素的梯度向量与等强度线相乘得到。接着，对置信度和特征项赋予不同权重并求和，获得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。然后，利用高光区域边缘像素的方差调整样本块窗口大小，基于样本块与最高优先级像素窗口的RGB色彩相似性和距离，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最高优先级的高光去除区域，以实现去除高光区域的目标。实验结果表明，所提方法可有效去除WCE图像中的高光，与Crinimisi算法和DeepGin方法相比，高光去除区域的变异系数更低。此外，高光去除区域的色彩和纹理与周围区域相似，且纹理具有连续性。