Awesome
Invertible Image Rescaling
This is the PyTorch implementation of paper: Invertible Image Rescaling (ECCV 2020 Oral). [link][arxiv].
2022/10 Update: Our paper "Invertible Rescaling Network and Its Extensions" has been accepted by IJCV. [link][arxiv]. We update the repository for experiments in the paper. The previous version can be found in the ECCV branch.
Dependencies and Installation
- Python 3 (Recommend to use Anaconda)
- PyTorch >= 1.0
- NVIDIA GPU + CUDA
- Python packages:
pip install numpy opencv-python lmdb pyyaml - TensorBoard:
- PyTorch >= 1.1:
pip install tb-nightly future - PyTorch == 1.0:
pip install tensorboardX
- PyTorch >= 1.1:
Dataset Preparation
Commonly used training and testing datasets can be downloaded here.
Get Started
Training and testing codes are in 'codes/'. Please see 'codes/README.md' for basic usages.
Invertible Architecture
Quantitative Results
Quantitative evaluation results (PSNR / SSIM) of different downscaling and upscaling methods for image reconstruction on benchmark datasets: Set5, Set14, BSD100, Urban100, and DIV2K validation set. For our method, differences on average PSNR / SSIM from different z samples are less than 0.02. We report the mean result over 5 draws.
| Downscaling & Upscaling | Scale | Param | Set5 | Set14 | BSD100 | Urban100 | DIV2K |
|---|---|---|---|---|---|---|---|
| Bicubic & Bicubic | 2x | / | 33.66 / 0.9299 | 30.24 / 0.8688 | 29.56 / 0.8431 | 26.88 / 0.8403 | 31.01 / 0.9393 |
| Bicubic & SRCNN | 2x | 57.3K | 36.66 / 0.9542 | 32.45 / 0.9067 | 31.36 / 0.8879 | 29.50 / 0.8946 | – |
| Bicubic & EDSR | 2x | 40.7M | 38.20 / 0.9606 | 34.02 / 0.9204 | 32.37 / 0.9018 | 33.10 / 0.9363 | 35.12 / 0.9699 |
| Bicubic & RDN | 2x | 22.1M | 38.24 / 0.9614 | 34.01 / 0.9212 | 32.34 / 0.9017 | 32.89 / 0.9353 | – |
| Bicubic & RCAN | 2x | 15.4M | 38.27 / 0.9614 | 34.12 / 0.9216 | 32.41 / 0.9027 | 33.34 / 0.9384 | – |
| Bicubic & SAN | 2x | 15.7M | 38.31 / 0.9620 | 34.07 / 0.9213 | 32.42 / 0.9028 | 33.10 / 0.9370 | – |
| TAD & TAU | 2x | – | 38.46 / – | 35.52 / – | 36.68 / – | 35.03 / – | 39.01 / – |
| CNN-CR & CNN-SR | 2x | – | 38.88 / – | 35.40 / – | 33.92 / – | 33.68 / – | – |
| CAR & EDSR | 2x | 51.1M | 38.94 / 0.9658 | 35.61 / 0.9404 | 33.83 / 0.9262 | 35.24 / 0.9572 | 38.26 / 0.9599 |
| IRN (ours) | 2x | 1.66M | 43.99 / 0.9871 | 40.79 / 0.9778 | 41.32 / 0.9876 | 39.92 / 0.9865 | 44.32 / 0.9908 |
| Downscaling & Upscaling | Scale | Param | Set5 | Set14 | BSD100 | Urban100 | DIV2K |
|---|---|---|---|---|---|---|---|
| Bicubic & Bicubic | 4x | / | 28.42 / 0.8104 | 26.00 / 0.7027 | 25.96 / 0.6675 | 23.14 / 0.6577 | 26.66 / 0.8521 |
| Bicubic & SRCNN | 4x | 57.3K | 30.48 / 0.8628 | 27.50 / 0.7513 | 26.90 / 0.7101 | 24.52 / 0.7221 | – |
| Bicubic & EDSR | 4x | 43.1M | 32.62 / 0.8984 | 28.94 / 0.7901 | 27.79 / 0.7437 | 26.86 / 0.8080 | 29.38 / 0.9032 |
| Bicubic & RDN | 4x | 22.3M | 32.47 / 0.8990 | 28.81 / 0.7871 | 27.72 / 0.7419 | 26.61 / 0.8028 | – |
| Bicubic & RCAN | 4x | 15.6M | 32.63 / 0.9002 | 28.87 / 0.7889 | 27.77 / 0.7436 | 26.82 / 0.8087 | 30.77 / 0.8460 |
| Bicubic & ESRGAN | 4x | 16.3M | 32.74 / 0.9012 | 29.00 / 0.7915 | 27.84 / 0.7455 | 27.03 / 0.8152 | 30.92 / 0.8486 |
| Bicubic & SAN | 4x | 15.7M | 32.64 / 0.9003 | 28.92 / 0.7888 | 27.78 / 0.7436 | 26.79 / 0.8068 | – |
| TAD & TAU | 4x | – | 31.81 / – | 28.63 / – | 28.51 / – | 26.63 / – | 31.16 / – |
| CAR & EDSR | 4x | 52.8M | 33.88 / 0.9174 | 30.31 / 0.8382 | 29.15 / 0.8001 | 29.28 / 0.8711 | 32.82 / 0.8837 |
| IRN (ours) | 4x | 4.35M | 36.19 / 0.9451 | 32.67 / 0.9015 | 31.64 / 0.8826 | 31.41 / 0.9157 | 35.07 / 0.9318 |
Qualitative Results
Acknowledgement
The code is based on BasicSR, with reference of FrEIA.
Contact
If you have any questions, please contact mingqing_xiao@pku.edu.cn.