mlsanityData to 13 Oct 2026

Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN

arXiv 2110.02321 ↗First posted 5 Oct 2021eess.IV cs.CVcited by 0

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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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 5 Oct 2021.

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5 of 18 references.

References · 5

  1. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,221
  2. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  3. 2019Deep Learning for Image Super-resolution: A Surveycited by 368
  4. 2021Paint Transformer: Feed Forward Neural Painting with Stroke Predictioncited by 61
  5. 2019Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)cited by 7

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