There are some implements of image super-resolution methods with Pytorch.
- AdderSR in addersr.py (AdderSR: Towards Energy Efficient Image Super-Resolution) (2021)
- SMSR in smsr.py (Learning Sparse Masks for Efficient Image Super-Resolution) (2021)
- HAN in han.py (Single Image Super-Resolution via a Holistic Attention Network) (2020)
- SAN in san.py (Second-order Attention Network for Single Image Super-Resolution) (2020)
- CSNLA in csnla.py (Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining) (2020)
- DRN in drn.py (Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution) (2020)
- RCAN in rcan.py (Image Super-Resolution Using Very Deep Residual Channel Attention Networks) (2019)
- DBPN in dbpn.py (Deep Back-Projection Networks For Super-Resolution) (2018)
- RDN in rdn.py (Residual Dense Network for Image SR) (2018)
- EDSR in edsr.py (Enhanced Deep Residual Networks for Single Image Super-Resolution) (2017)
- SRResNet in srresnet.py (Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network) (2016)
- SRCNN in srcnn.py (Image Super-Resolution Using Deep Convolutional Networks) (2014)
- Python 3.5.3 +
- Pytorch 1.1.0
- tensorboardX
- pytorch-colors
Image-based algoritnms.
- Train:
python main.py. - Test:
python test.py. More details inoption.py.
Patch-based algoritnms.
-
Image to patch:
python image_to_patch.py. -
Train:
python main.py. -
Test:
python test.py. More details inoption.py. -
tensorborad --logdir ./log
Plot the LAM map
python ./LAM/LAM_main.py. (Interpreting Super-Resolution Networks with Local Attribution Maps)
Image-based algoritnms.
| Algorithms | PSNR |
|---|---|
| Bicubic | 36.38 |
| EDSR_original | 39.81 |
| EDSR+b16k64 | 39.85 |
| EDSR+b32k256 | 40.05 |
| SRResNet without BN | 40.04 |
| RDN | 39.90 |
| DBPN | 40.14 |
| RCAN | 40.03 |
| SAN | 39.97 |
| HAN | 40.04 |
| SMSR | 39.86 |
Patch-based algoritnms.
| Algorithms | Bicubic | SRCNN_original | SRCNN | VDSR_original |
|---|---|---|---|---|
| PSNR | 36.38 | 38.58 | 38.61 | 39.54 |
This project is released under the Apache 2.0 license.