Awesome

CUGAN

Toward Interactive Modulation for Photo-Realistic Image Restoration. (Paper Link)

By Haoming Cai*, Jingwen He*, Yu Qiao, and Chao Dong in CVPRW, NTIRE workshop 2021.

<p align="center"> <img src="figures/modulation.png"> Two-dimension Modulation </p> <p align="center"> <img src="figures/modulation_real.png"> Real-World Modulation </p>

Dependencies and Installation

pip install -r requirements.txt
Python 3 (Recommend to use Anaconda)
PyTorch >= 1.3
NVIDIA GPU + CUDA

How to Test

Prepare the test dataset
1. Download LIVE1 dataset and CBSD68 dataset from Google Drive
2. Generate LQ images with different combinations of degradations using matlab codes/data_scripts/generate_2D_val.m.
Download the pretrained model
1. Download pretrained CUGAN from Google Drive
2. Modify the pretrain_model_G in configuration file options/test/xxxxxx.yml.
Test CUGAN with range of restoration strength
1. Modify the configuration file options/test/modulation_CUGAN.yml. ❗️Importantly, cond_init, range_mode, range_stride are crucial in this testing mode.
2. Run command:
```
cd codes
python test-cugan_range-cond.py -opt options/test/modulation_CUGAN.yml
```
Test CUGAN with specific restoration strength
1. Modify the configuration file options/test/test_CUGAN.yml. ❗️Importantly, cond is crucial in this testing mode.
2. Run command:
```
cd codes
python test-cugan_specific-cond.py -opt options/test/test_CUGAN.yml
```

How to Train

Cooming Soon

Acknowledgement

This code is based on mmsr, BasicSR and CResMD.