Manga, as a widely beloved form of entertainment around the world, have shifted from paper to electronic screens with the proliferation of handheld devices. However, as the demand for image quality increases with screen development, high-quality images can hinder transmission and affect the viewing experience. Traditional vectorization methods require a significant amount of manual parameter adjustment to process screentone. Using deep learning, lines and screentone can be automatically extracted and image resolution can be enhanced. Super-resolution can convert low-resolution images to high-resolution images while maintaining low transmission rates and providing high-quality results. However, traditional Super Resolution methods for improving manga resolution do not consider the meaning of screentone density, resulting in changes to screentone density and loss of meaning. In this paper, we aims to address this issue by first classifying the regions and lines of different screentone in the manga using deep learning algorithm, then using corresponding super-resolution models for quality enhancement based on the different classifications of each block, and finally combining them to obtain images that maintain the meaning of screentone and lines in the manga while improving image resolution.

翻译：漫画作为一种全球广受欢迎的娱乐形式，随着手持设备的普及，已从纸质媒介转向电子屏幕。然而，随着屏幕发展对图像质量需求的提升，高分辨率图像可能会阻碍传输并影响观看体验。传统矢量化方法需要大量手动参数调整来处理网点。利用深度学习，可以自动提取线条和网点，并提升图像分辨率。超分辨率技术可将低分辨率图像转换为高分辨率图像，同时保持低传输速率并提供高质量结果。然而，传统提升漫画分辨率的超分辨率方法未考虑网点密度的含义，导致网点密度改变及含义丢失。本文旨在解决此问题，首先通过深度学习算法对漫画中不同网点的区域和线条进行分类，然后根据各区块的不同分类使用相应的超分辨率模型进行质量增强，最后将其融合，以获得在提升图像分辨率的同时保持漫画网点与线条含义的图像。