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PyTorch implementation of Accelerating the Super-Resolution Convolutional Neural Network (ECCV 2016)
| Date | Stars |
|---|---|
| 2026-07-31 | 279 |
| 2026-08-06 | 279 |
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# FSRCNN
This repository is implementation of the ["Accelerating the Super-Resolution Convolutional Neural Network"](https://arxiv.org/abs/1608.00367).
<center><img src="./thumbnails/fig1.png"></center>
## Differences from the original
- Added the zero-padding
- Used the Adam instead of the SGD
## 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](https://www.dropbox.com/s/01z95js39kgw1qv/91-image_x2.h5?dl=0) |
| 91-image | 3 | Train | [Download](https://www.dropbox.com/s/qx4swlt2j7u4twr/91-image_x3.h5?dl=0) |
| 91-image | 4 | Train | [Download](https://www.dropbox.com/s/vobvi2nlymtvezb/91-image_x4.h5?dl=0) |
| Set5 | 2 | Eval | [Download](https://www.dropbox.com/s/4kzqmtqzzo29l1x/Set5_x2.h5?dl=0) |
| Set5 | 3 | Eval | [Download](https://www.dropbox.com/s/kyhbhyc5a0qcgnp/Set5_x3.h5?dl=0) |
| Set5 | 4 | Eval | [Download](https://www.dropbox.com/s/ihtv1acd48cof14/Set5_x4.h5?dl=0) |
Otherwise, you can use `prepare.py` to create custom dataset.
```bash
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-3 \
--batch-size 16 \
--num-epochs 20 \
--num-workers 8 \
--seed 123
```
## Test
Pre-trained weights can be downloaded from the links below.
| Model | Scale | Link |
|-------|-------|------|
| FSRCNN(56,12,4) | 2 | [Download](https://www.dropbox.com/s/1k3dker6g7hz76s/fsrcnn_x2.pth?dl=0) |
| FSRCNN(56,12,4) | 3 | [Download](https://www.dropbox.com/s/pm1ed2nyboulz5z/fsrcnn_x3.pth?dl=0) |
| FSRCNN(56,12,4) | 4 | [Download](https://www.dropbox.com/s/vsvumpopupdpmmu/fsrcnn_x4.pth?dl=0) |
The results are stored in the same path as the query image.
```bash
python test.py --weights-file "BLAH_BLAH/fsrcnn_x3.pth" \
--image-file "data/butterfly_GT.bmp" \
--scale 3
```
## Results
PSNR was calculated on the Y channel.
### Set5
| Eval. Mat | Scale | Paper | Ours (91-image) |
|-----------|-------|-------|-----------------|
| PSNR | 2 | 36.94 | 37.12 |
| PSNR | 3 | 33.06 | 33.22 |
| PSNR | 4 | 30.55 | 30.50 |
<table>
<tr>
<td><center>Original</center></td>
<td><center>BICUBIC x3</center></td>
<td><center>FSRCNN x3 (34.66 dB)</center></td>
</tr>
<tr>
<td>
<center><img src="./data/lenna.bmp""></center>
</td>
<td>
<center><img src="./data/lenna_bicubic_x3.bmp"></center>
</td>
<td>
<center><img src="./data/lenna_fsrcnn_x3.bmp"></center>
</td>
</tr>
<tr>
<td><center>Original</center></td>
<td><center>BICUBIC x3</center></td>
<td><center>FSRCNN x3 (28.55 dB)</center></td>
</tr>
<tr>
<td>
<center><img src="./data/butterfly_GT.bmp""></center>
</td>
<td>
<center><img src="./data/butterfly_GT_bicubic_x3.bmp"></center>
</td>
<td>
<center><img src="./data/butterfly_GT_fsrcnn_x3.bmp"></center>
</td>
</tr>
</table>
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matched fp:c01c05dd48857a33, llm:Topics: image-super-resolution; Description: PyTorch implementation of Accelerating the Super-Resolution Convolutional Neural Network (ECCV 2016)
matched fp:c01c05dd48857a33, llm:Topics: image-super-resolution; Description: PyTorch implementation of Accelerating the Super-Resolution Convolutional Neural Network (ECCV 2016)