SRCNN
April 22, 2019 ยท View on GitHub
This repository is implementation of the "Image Super-Resolution Using Deep Convolutional Networks".
Differences from the original
- Added the zero-padding
- Used the Adam instead of the SGD
- Removed the weights initialization
Requirements
- PyTorch 1.0.0
- Numpy 1.15.4
- Pillow 5.4.1
- h5py 2.8.0
- tqdm 4.30.0
Train
The 91-image, Set5 dataset converted to HDF5 can be downloaded from the links below.
| Dataset | Scale | Type | Link |
|---|---|---|---|
| 91-image | 2 | Train | Download |
| 91-image | 3 | Train | Download |
| 91-image | 4 | Train | Download |
| Set5 | 2 | Eval | Download |
| Set5 | 3 | Eval | Download |
| Set5 | 4 | Eval | Download |
Otherwise, you can use prepare.py to create custom dataset.
python train.py --train-file "BLAH_BLAH/91-image_x3.h5" \
--eval-file "BLAH_BLAH/Set5_x3.h5" \
--outputs-dir "BLAH_BLAH/outputs" \
--scale 3 \
--lr 1e-4 \
--batch-size 16 \
--num-epochs 400 \
--num-workers 8 \
--seed 123
Test
Pre-trained weights can be downloaded from the links below.
| Model | Scale | Link |
|---|---|---|
| 9-5-5 | 2 | Download |
| 9-5-5 | 3 | Download |
| 9-5-5 | 4 | Download |
The results are stored in the same path as the query image.
python test.py --weights-file "BLAH_BLAH/srcnn_x3.pth" \
--image-file "data/butterfly_GT.bmp" \
--scale 3
Results
We used the network settings for experiments, i.e., .
PSNR was calculated on the Y channel.
Set5
| Eval. Mat | Scale | SRCNN | SRCNN (Ours) |
|---|---|---|---|
| PSNR | 2 | 36.66 | 36.65 |
| PSNR | 3 | 32.75 | 33.29 |
| PSNR | 4 | 30.49 | 30.25 |
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