This repository is implementation of the "Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections".
To reduce computational cost, it adopts stride 2 for the first convolution layer and the last transposed convolution layer.
- PyTorch
- Tensorflow
- tqdm
- Numpy
- Pillow
Tensorflow is required for quickly fetching image in training phase.
| Input | JPEG (Quality 10) |
|
|
| AR-CNN | RED-Net 10 |
|
|
| RED-Net 20 | RED-Net 30 |
|
|
When training begins, the model weights will be saved every epoch.
If you want to train quickly, you should use --use_fast_loader option.
python main.py --arch "REDNet30" \ # REDNet10, REDNet20, REDNet30
--images_dir "" \
--outputs_dir "" \
--jpeg_quality 10 \
--patch_size 50 \
--batch_size 16 \
--num_epochs 20 \
--lr 1e-4 \
--threads 8 \
--seed 123 \
--use_fast_loader Output results consist of image compressed with JPEG and image with artifacts reduced.
python example --arch "REDNet30" \ # REDNet10, REDNet20, REDNet30
--weights_path "" \
--image_path "" \
--outputs_dir "" \
--jpeg_quality 10 
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent