# SRCNN-pytorch
**Repository Path**: zongfang/SRCNN-pytorch
## Basic Information
- **Project Name**: SRCNN-pytorch
- **Description**: 转到码云，方便墙内访问
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 1
- **Forks**: 0
- **Created**: 2022-03-28
- **Last Updated**: 2023-11-03
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# SRCNN
This repository is implementation of the ["Image Super-Resolution Using Deep Convolutional Networks"](https://arxiv.org/abs/1501.00092).
## 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](https://www.dropbox.com/s/2hsah93sxgegsry/91-image_x2.h5?dl=0) |
| 91-image | 3 | Train | [Download](https://www.dropbox.com/s/curldmdf11iqakd/91-image_x3.h5?dl=0) |
| 91-image | 4 | Train | [Download](https://www.dropbox.com/s/22afykv4amfxeio/91-image_x4.h5?dl=0) |
| Set5 | 2 | Eval | [Download](https://www.dropbox.com/s/r8qs6tp395hgh8g/Set5_x2.h5?dl=0) |
| Set5 | 3 | Eval | [Download](https://www.dropbox.com/s/58ywjac4te3kbqq/Set5_x3.h5?dl=0) |
| Set5 | 4 | Eval | [Download](https://www.dropbox.com/s/0rz86yn3nnrodlb/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-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](https://www.dropbox.com/s/rxluu1y8ptjm4rn/srcnn_x2.pth?dl=0) |
| 9-5-5 | 3 | [Download](https://www.dropbox.com/s/zn4fdobm2kw0c58/srcnn_x3.pth?dl=0) |
| 9-5-5 | 4 | [Download](https://www.dropbox.com/s/pd5b2ketm0oamhj/srcnn_x4.pth?dl=0) |
The results are stored in the same path as the query image.
```bash
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 |
| Original |
BICUBIC x3 |
SRCNN x3 (27.53 dB) |
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| Original |
BICUBIC x3 |
SRCNN x3 (29.30 dB) |
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| Original |
BICUBIC x3 |
SRCNN x3 (28.58 dB) |
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