ESPCN_paddle
July 11, 2023 · View on GitHub
This repository is implementation of the "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network".
参考：https://github.com/yjn870/ESPCN-pytorch
Requirements
- paddlepaddle 2.4.0
- paddleseg 2.8.0
- 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 | 3 | Train | Download |
| Set5 | 3 | 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-3 \
--batch-size 16 \
--num-epochs 50 \
--num-workers 0 \
--seed 123
Test
Pre-trained weights can be found in BLAH_BLAH/outputs.
The results are stored in the same path as the query image.
python test.py --weights-file "BLAH_BLAH/outputs/x3/best.pdiparams" \
--image-file "data/butterfly_GT.bmp" \
--scale 3
Results
PSNR was calculated on the Y channel.
Set5
| Eval. Mat | Scale | Paper |
|---|---|---|
| PSNR | 3 | 33.06 |
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