Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN

TL;DR

This paper presents a modified super-resolution CNN model specifically designed to enlarge and enhance anime images, achieving better quality than existing methods.

Contribution

The study introduces a novel CNN-based model tailored for anime image enlargement, outperforming traditional super-resolution techniques in quality.

Findings

Enhanced image quality over existing methods

Successful enlargement of anime images with improved clarity

Outperforms original SRCNN in experiments

Abstract

Anime is a storytelling medium similar to movies and books. Anime images are a kind of artworks, which are almost entirely drawn by hand. Hence, reproducing existing Anime with larger sizes and higher quality images is expensive. Therefore, we proposed a model based on convolutional neural networks to extract outstanding features of images, enlarge those images, and enhance the quality of Anime images. We trained the model with a training set of 160 images and a validation set of 20 images. We tested the trained model with a testing set of 20 images. The experimental results indicated that our model successfully enhanced the image quality with a larger image-size when compared with the common existing image enlargement and the original SRCNN method.