README.md

August 11, 2023 ยท View on GitHub

Conditional Stochastic Normalizing Flows for Blind Super-Resolution of Remote Sensing Images

by Hanlin Wu, Ning Ni, Shan Wang, and Libao Zhang, details are in paper.

Usage

Clone the repository:

git clone https://github.com/hanlinwu/BlindSRSNF.git

Requirements:

  • pytorch==1.13.0
  • pytorch-lightning==1.5.5
  • numpy
  • opencv-python
  • easydict
  • tqdm

Test with our pretrained models

  1. Download the checkpoints from this url.
  2. Unzip the downloaded file, and put the files on path: logs/

Train:

  1. Download the training datsets from this url.

  2. Unzip the downloaded dataset, and put the files on path: load/

  3. Do training:

    For ansio degradation:

    python train.py --config configs/blindsrsnf_aniso.yaml

    For iso degradation:

    python train.py --config configs/blindsrsnf_iso.yaml

    For ansio degradation with the WorldStrat dataset:

    python train.py --config configs/blindsrsnf_iso.yaml

Test:

python test_diff.py --checkpoint logs/your_checkpoint_path

or

sh scripts/test_diff_aniso.sh logs/your_checkpoint_path