maxres

動漫插畫放大器

Requirements

• Tensorflow 2.2.0
• Numpy
• Opencv-python
• Flask

Use

Upscaling

• 2x:
  py maxres.py 2x img_path

• 4x:
  py maxres.py 4x img_path

• 8x: py maxres.py 8x img_path

Jpeg denoise

• Very low (Quality: 90 - 100):
  py maxres.py jpeg-verylow img_path

• Low (Quality: 75 - 89):
  py maxres.py jpeg-low img_path

• Medium (Quality: 55 - 74):
  py maxres.py jpeg-medium img_path

• High (Quality: 30 - 54):
  py maxres.py jpeg-high img_path

• Very High (Quality: 1 - 29):
  py maxres.py jpeg-veryhigh img_path

Denoise

• Light:
  py maxres.py denoise-light img_path

• Medium:
  py maxres.py denoise-medium img_path

• Strong:
  py maxres.py denoise-strong img_path

Web UI/API

py webapi.py

Then go to your host :5000.

References

[1] Lim, B., Son, S., Kim, H., Nah, S., & Mu Lee, K. (2017). Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 136-144).

[2] Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2), 295-307.

[3] Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).