Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN
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Abstract (arXiv)
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.
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References · 5
- 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,221
- 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
- 2019Deep Learning for Image Super-resolution: A Surveycited by 368
- 2021Paint Transformer: Feed Forward Neural Painting with Stroke Predictioncited by 61
- 2019Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)cited by 7
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