This repository is an official PyTorch implementation of the paper "Efficiently Reconstructing High-Quality Details of 3D Digital Rocks with Super-Resolution Transformer ".
The source code is primarily derived from EDSR. We provide full training and testing codes. You can train your model from scratch, or use a pre-trained model to enlarge your digital rock images. We will upload the pre-trained model soon.
- Python 3.8.5
- PyTorch = 2.0.1
- numpy
- cv2
- skimage
- tqdm
git clone ### Quick Start
```bash
git clone https://github.com/MHDXing/MASR-for-Digital-Rock-Images.git
cd EAST-for-3D-Digital-Rocks-main/src
The dataset we used was derived from DeepRockSR-3D. There are 2400, 300, 300 HR 3D images (100x100x100) for training, testing and validation, respectively.
- Download the dataset and unpack them to any place you want. Then, change the
dir_data
argument in./options.py
ordemo.sh
to the place where images are located - You can change the hyperparameters of different models by modifying the files in the
./options.py
- Run
main.py
using script filedemo.sh
bash demo.sh
- You can find the results in
./experiments/EAST
if thesave
argument in./options
isEAST
.
- Download our pre-trained models to
./models
folder or use your pre-trained models - Change the
dir_data
argument in./options.py
ordemo.sh
to the place where images are located - Run
main.py
using script filedemo.sh
bash demo.sh
- You can find the enlarged images in
./experiments/results
folder.