Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN
This work addresses the cost issue for anime creators and enthusiasts by providing an automated solution for image enlargement, though it appears incremental as it builds upon existing SRCNN methods.
The paper tackled the problem of expensive manual enlargement of anime images by proposing a convolutional neural network model that successfully enhanced image quality and size, outperforming common image enlargement methods and the original SRCNN.
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.