English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Manga, as a widely beloved form of entertainment around the world, haveshifted from paper to electronic screens with the proliferation of handhelddevices. However, as the demand for image quality increases with screendevelopment, high-quality images can hinder transmission and affect the viewingexperience. Traditional vectorization methods require a significant amount ofmanual parameter adjustment to process screentone. Using deep learning, linesand screentone can be automatically extracted and image resolution can beenhanced. Super-resolution can convert low-resolution images to high-resolutionimages while maintaining low transmission rates and providing high-qualityresults. However, traditional Super Resolution methods for improving mangaresolution do not consider the meaning of screentone density, resulting inchanges to screentone density and loss of meaning. In this paper, we aims toaddress this issue by first classifying the regions and lines of differentscreentone in the manga using deep learning algorithm, then using correspondingsuper-resolution models for quality enhancement based on the differentclassifications of each block, and finally combining them to obtain images thatmaintain the meaning of screentone and lines in the manga while improving imageresolution.

Screentone-Aware Manga Super-Resolution Using DeepLearning