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Official implementation of Unfolded Deep Kernel Estimation for Blind Image Super-resolution.

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Unfolded Deep Kernel Estimation for Blind Image Super-resolution

Hongyi Zheng, Hongwei Yong, Lei Zhang, "Unfolded Deep Kernel Estimation for Blind Image Super-resolution". Accepted by ECCV2022.

[paper] [supp]

The implementation of UDKE is based on the awesome Image Restoration Toolbox [KAIR].

Requirement

  • PyTorch 1.9+
  • prettytable
  • tqdm

Testing

Step 1

  • Download testing kernels from [OneDrive].
  • Unzip downloaded testing kernels and put the folders into ./kernels/test
  • Download pretrained models from [OneDrive].
  • Unzip downloaded file and put the folders into ./release/udke

Step 2

Configure options/test_udke.json. Important settings:

  • task: task name.
  • path/root: path to save the tasks.
  • path/pretrained_netG: path to the folder containing the pretrained models.
  • data/test/sigma: noise level
  • data/test/sf: scale factor
  • data/test/dataroot_h: path to testing sets

Step 3

python test_udke.py

Training

Step 1

Configure options/train_udke.json. Important settings:

  • task: task name.
  • path/root: path to save the tasks.
  • data/train/sigma: noise level
  • data/train/sf: scale factor
  • data/train/dataroot_h: path to traing sets
  • data/test/sigma: noise level
  • data/test/sf: scale factor
  • data/test/dataroot_h: path to testing sets

Step 2

python train_udke.py

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