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.

本文提出了一种适用于胶囊内窥镜图像的去除高光区域的方法，通过计算高光区边缘的置信度和特征项，确定优先级，使用最佳匹配块在已知区域内复制像素，从而实现去除高光区域并保持颜色和纹理连续的目标。实验结果表明，该方法有效地去除了胶囊内窥镜图像中的高光，并且具有与周围区域相似的颜色和纹理。

一种胶囊内镜图像高光去除方法

A Highlight Removal Method for Capsule Endoscopy Images

The task of human reposing involves generating a realistic image of a person
standing in an arbitrary conceivable pose. There are multiple difficulties in
generating perceptually accurate images, and existing methods suffer from
limitations in preserving texture, maintaining pattern coherence, respecting
cloth boundaries, handling occlusions, manipulating skin generation, etc. These
difficulties are further exacerbated by the fact that the possible space of
pose orientation for humans is large and variable, the nature of clothing items
is highly non-rigid, and the diversity in body shape differs largely among the
population. To alleviate these difficulties and synthesize perceptually
accurate images, we propose VGFlow. Our model uses a visibility-guided flow
module to disentangle the flow into visible and invisible parts of the target
for simultaneous texture preservation and style manipulation. Furthermore, to
tackle distinct body shapes and avoid network artifacts, we also incorporate a
self-supervised patch-wise "realness" loss to improve the output. VGFlow
achieves state-of-the-art results as observed qualitatively and quantitatively
on different image quality metrics (SSIM, LPIPS, FID).

本研究提出了一个名为 VGFlow 的技术以生成具有真实感的人体姿势图像，并通过使用可见性引导的流模块来实现同时纹理保护和样式操作。VGFlow 在不同的图像质量度量（SSIM，LPIPS，FID）上观察到了定性和定量的最先进结果。