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

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Deep Learning UDKE
Overview

Unfolded Deep Kernel Estimation for Blind Image Super-resolution

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

[arxiv]

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
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