HDR-DSP is the first joint super-resolution and HDR neural network for push-frame satellites. HDR-DSP can be trained on real data thanks to self-supervised learning.
- Install pytorch and torchvision
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
- Download the SkySat multi-exposure data here.
wget https://github.com/centreborelli/HDR-DSP-SR/releases/download/v1/hdr-dsp-real-dataset.zip
unzip hdr-dsp-real-dataset.zip
- Preprocess the data using the notebook RemoveSaturation.ipynb to remove saturated frames and to categorize sequences by their length.
The command
python train.py
launches the training the HDR-DSP super-resolution network (see train.py file for more options). It requires pre-trained weights for the motion estimation sub-network stored in a file pretrained_Fnet.pth.tar. We provide our pre-trained weights, but if you want to train it yourself you can do it with the command:
python train_FNet.py
python test.py
If you use this code please cite the following papers:
Self-supervised super-resolution for multi-exposure push-frame satellites, Ngoc Long Nguyen, Jérémy Anger, Axel Davy, Pablo Arias, Gabriele Facciolo; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Self-Supervised Multi-Image Super-Resolution for Push-Frame Satellite Images, Ngoc Long Nguyen, Jeremy Anger, Axel Davy, Pablo Arias, Gabriele Facciolo; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021.