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CResMD

Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration Paper

By Jingwen He*, Chao Dong*, and Yu Qiao (* indicates equal contribution)

<p align="center"> <img src="figures/2D_modulation.png"> two-dimension modulation </p> <p align="center"> <img src="figures/3D_modulation.png"> three-dimension modulation </p> <h2 align="center"> Demo video of two-dimension modulation. </h2> <p align="center"> <a href="https://www.youtube.com/watch?v=GHkGOkqf1tU" target="_blank"> <img src="figures/cover.png" > </a></p>

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

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, codes/data_scripts/generate_3D_val.m.
Modulation Testing
1. (optional) Modify the configuration file options/test/modulation_CResMD.yml.
- dataroot_GT, dataroot_LQ, pretrain_model_G.
- cond_init: The starting point of modulation, usually set to [0, 0].
- modulation_dim: The dimension you would like to modulate.
- modulation_stride: The stride for modulation process, usually set to 0.1.
1. Run command:
```
cd codes
python modulation_CResMD.py -opt options/test/modulation_CResMD.yml
```
Test CResMD
1. (optional) Modify the configuration file options/test/test_CResMD.yml.
- dataroot_GT, dataroot_LQ
- cond_norm (This paper uses [40, 50] for 2D modulation, [40, 50, 92] for 3D modulation).
- mode (LQGT or LQGT_cond. if it is set to LQGT, you should specify cond, which is the degradation levels.)
1. Run command:
```
python test.py -opt options/test/test_CResMD.yml
```
Test base network
1. Modify the configuration file options/test/test_Base.yml.
2. Run command:
```
python test.py -opt options/test/test_Base.yml
```

How to Train

CResMD
1. Prepare datasets, usually the DIV2K dataset. More details are in codes/data.
2. Modify the configuration file options/train/train_CResMD.yml
- dataroot_GT, dataroot_LQ
- cond_norm (This paper uses [40, 50] for 2D modulation, [40, 50, 92] for 3D modulation).
1. Run command:
```
python train_CResMD.py -opt options/train/train_CResMD.yml
```
base network
1. Prepare datasets, usually the DIV2K dataset. More details are in codes/data.
2. Modify the configuration file options/train/train_Base.yml
3. Run command:
```
python train.py -opt options/train/train_Base.yml
```

Acknowledgement

This code is based on mmsr.