Exploiting Aliasing for Manga Restoration

CVPR Paper | Arxiv | Project Website | BibTex

NOTE

This is a modified version made by me, bycloud. Please refer the original author here for credit.

Example Results

Belows shows an example of our restored manga image. The image comes from the Manga109 dataset.

Setup

Follow this YouTube tutorial or if you have any questions feel free to join my discord and ask there.

Start

Clone this repository and place it anywhere you want on your PC.

Pretrained models

Download the models below and create a folder called release_model/ and put it under there.

MangaRestoration

Setup environment

We are going to use Anaconda3, download Anaconda3 if you don't have it.

Create conda environment:

conda create -n EAMR python=3.6
conda activate EAMR

2-1. Setup conda env for nvidia non-30 series GPU:

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

2-2. Setup conda env for nvidia 30 series GPU:

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

2-3. Setup conda env for non-nvidia GPU:

conda install pytorch torchvision torchaudio cpuonly -c pytorch

Install the dependencies

cd WHERE_YOU_CLONED_THIS_REPO
pip install -r requirements.txt

To reuse the created conda environment after you close the prompt, you just need to:

conda activate EAMR

Testing

Create folder datazip/manga1/test/ and flist/manga1/
Place your test images under datazip/manga1/test/
Run:

python scripts/flist.py --path datazip/manga1/test --output flist/manga1/test.flist

This generates a test.flist for your test images

Run:

python testreal.py -c configs/manga.json -n resattencv -s 256

and your results will be under MangaRestoration\release_model\resattencv_manga_cons256\results_real_00400\

Introduction

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screentone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative two-stage method to restore quality bitonal manga from degraded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SE-Net) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method.

Copyright and License

You are granted with the LICENSE for both academic and commercial usages.

Citation

If any part of our paper and code is helpful to your work, please generously cite with:

@inproceedings{xie2021exploiting,
  author = {Minshan Xie and Menghan Xia and Tien-Tsin Wong},
  title = {Exploiting Aliasing for Manga Restoration},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition},
  year = {2021},
  pages = {13405--13414}
}

bycloudai/MangaRestoration-Windows