Department of Electrical Engineering, National Yunlin University of Science and Technology, Taiwan.
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taiwan.
Comput Methods Programs Biomed. 2020 Aug;192:105414. doi: 10.1016/j.cmpb.2020.105414. Epub 2020 Feb 28.
In this article, a content-aware specular reflection suppression scheme was developed based on adaptive image inpainting and neural network for endoscopic images. To decrease the impact of specular reflection on visual quality, the proposed scheme consists of three parts: reflection detection, reflection region classification, and reflection concealment. To automatically locate specular reflection regions, a thresholding algorithm with a morphological dilation operation is employed. To reduce the effect of specular reflection, an adaptive image inpainting algorithm is devised to deal with different reflection regions. To achieve content-aware image inpainting, a reflection region classification algorithm is designed by analyzing the local image content to adjust the parameters in the proposed image inpainting algorithm. The experimental results demonstrate that the proposed scheme can automatically and correctly not only locate but also conceal specular reflection regions in endoscopic images. Furthermore, since the average SSIM (structural similarity index) value of the proposed scheme is higher than those of the existing methods, our specular reflection suppression scheme is superior to the existing methods.
本文提出了一种基于自适应图像修复和神经网络的内窥镜图像镜面反射抑制方案。为了降低镜面反射对视觉质量的影响，该方案包括反射检测、反射区域分类和反射隐藏三个部分。为了自动定位镜面反射区域，采用了带形态学膨胀操作的阈值算法。为了减小镜面反射的影响，设计了一种自适应图像修复算法来处理不同的反射区域。为了实现基于内容的图像修复，通过分析局部图像内容设计了一种反射区域分类算法，以调整所提出的图像修复算法中的参数。实验结果表明，该方案不仅可以自动且正确地定位，还可以隐藏内窥镜图像中的镜面反射区域。此外，由于所提出方案的平均 SSIM（结构相似性指数）值高于现有方法，因此我们的镜面反射抑制方案优于现有方法。
