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A Transformer-Based Model for Super-Resolution of Anime Image.

Shizhuo Xu1, Vibekananda Dutta2, Xin He1

  • 1Graduate School of Information, Production and System, Waseda University, Kitakyushu 808-0135, Japan.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

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This study introduces an Anime Image Super-Resolution (AISR) method using Swin Transformers, enhancing low-frequency details and semantic information for superior anime image quality. The novel approach outperforms existing convolutional neural network and transformer-based methods in experiments.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Image Super-Resolution (ISR) enhances image quality but is underutilized in anime production.
  • Current ISR methods predominantly use Convolutional Neural Networks (CNNs), with limited exploration of Transformer architectures.

Purpose of the Study:

  • To develop a novel Anime Image Super-Resolution (AISR) method leveraging the Swin Transformer architecture.
  • To improve the quality and resolution of anime images through advanced deep learning techniques.

Main Methods:

  • Proposed an AISR method based on the Swin Transformer, incorporating shallow and deep feature extraction.
  • Enhanced low-frequency information with a Gaussian filter and introduced variable window sizes, modifying Swin Transformer's patch merging.
Keywords:
anime datasetanime imagedeep featurehigh-frequency informationimage reconstructionlow-frequency informationshallow featuresuper-resolutionswin transformer

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  • Utilized sub-pixel convolution for feature upsampling and channel expansion during image reconstruction.

    • Main Results:

    • Trained and tested the AISR model on a custom high-quality anime dataset for various magnifications (2×, 4×, 8×).
    • Achieved superior performance compared to conventional CNN-based and other transformer-based ISR methods.
    • Demonstrated effectiveness through numerical metrics (PSNR, SSIM) and graphical comparisons.

    Conclusions:

    • The proposed Swin Transformer-based AISR method significantly outperforms existing approaches for anime image enhancement.
    • The novel enhancements to the Swin Transformer architecture and the use of a dedicated anime dataset contribute to robust and high-quality results.