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Unsupervised Super Resolution for Sentinel-2 satellite imagery

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Thesis

Unsupervised Super Resolution for Sentinel-2 satellite imagery

In this work three unsupervised deep learning models were utilized for Super Resolving satellite imagery obtained from Sentinel-2 constellation.

1. Deep Image Prior (DIP)

The original implementation of this model was proposed by Ulyanov et al.

Requirments

  • python == 3.8
  • earthpy
  • numpy
  • pytorch
  • matplotlib
  • scikit-image
  • gdal
  • rasterio
  • jupyter notebook

2. Zero-Shot Super Resolution (ΖSSR)

The original implementation of this model was proposed by Shocher et al.

Requirments

  • python == 2.7
  • numpy
  • tensorflow
  • matplotlib
  • scikit-image
  • opencv-python
  • imageio

3. Degradation-Aware Super Resolution (DASR)

The original implementation of this modes was proposed by Wang et al.

Requirments

  • Python 3.6
  • PyTorch == 1.1.0
  • numpy
  • skimage
  • imageio
  • matplotlib
  • cv2

Train

1. Prepare training data

1.1 Download the DIV2K dataset and the Flickr2K dataset.

1.2 Combine the HR images from these two datasets in your_data_path/DF2K/HR to build the DF2K dataset.

2. Begin to train

Run ./main.sh to train on the DF2K dataset. Please update dir_data in the bash file as your_data_path.

Test

1. Prepare test data

Download benchmark datasets (e.g., Set5, Set14 and other test sets) and prepare HR/LR images in your_data_path/benchmark.

2. Begin to test

Run ./test.sh to test on benchmark datasets. Please update dir_data in the bash file as your_data_path.

Quick Test on An LR Image

Run ./quick_test.sh to test on an LR image. Please update img_dir in the bash file as your_img_path.

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