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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.
