Content-aware specular reflection suppression based on adaptive image inpainting and neural network for endoscopic images. (August 2020)

• Record Type: Journal Article
• Authors: Shen, Day-Fann; Guo, Jian-Jhih; Lin, Guo-Shiang; Lin, Jen-Yung
• Is Part Of: Computer methods and programs in biomedicine. Volume 192(2020)
• Publication Date: 2020-08
• Subjects: Specular reflection suppression -- Image inpainting -- Neural network; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105414
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A content-aware specular reflection suppression scheme based on adaptive image inpainting and neural network for endoscopic images was proposed. To reduce the impact of specular reflection on visual quality, the proposed scheme is comprised of three parts: reflection detection, reflection region classification, and reflection concealment. To suppress the effect of specular reflection, an adaptive image inpainting algorithm is developed 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. 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 [5–7] . Abstract: 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.