Wrapper for SwinIR

Based on this repository - the official PyTorch implementation of SwinIR: Image Restoration Using Swin Transformer.

About SwinIR and this repository:

• SwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction.
• This repository only provides usage of the 3 image super-resolution tasks from SwinIR at the moment.
• SwinIR.py is a minimal wrapper for the super resolution model, making it easy to use as a part from a bigger pipeline.
• Only enables the usage of the pretrained weights from the model zoo. For training, see the official repo.
• Uses the PyTorch model network_swinir.py from the official repo (unchanged).

How to Use

• For easy usage on your own data, see Demo.ipynb. This notebook will be displayed best using Google Colab which supports more interactive usage:

• As a quick preview, this example demonstrates usage with only few lines:

  import cv2
  from SwinIR_wrapper import SwinIR_SR
  
  # initialize super resolution model
  sr = SwinIR_SR(model_type='real_sr', scale=4)
  
  # load low quality image
  img_lq = cv2.imread(path, cv2.IMREAD_COLOR)
  
  # feed the image to the SR model
  img_hq = sr.upscale(img_lq)

License and Acknowledgement

Please follow the license of the official repo of this paper. Thanks for their great work!