https://github.com/hejingwenhejingwen/cresmd
(ECCV 2020) Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration
https://github.com/hejingwenhejingwen/cresmd
cresmd deblurring deep-learning denoising dynamic-networks image-restoration jpeg-deblocking low-level-vision
Last synced: 5 months ago
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(ECCV 2020) Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration
- Host: GitHub
- URL: https://github.com/hejingwenhejingwen/cresmd
- Owner: hejingwenhejingwen
- Created: 2019-12-11T14:02:48.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-28T10:23:44.000Z (about 5 years ago)
- Last Synced: 2023-03-05T16:20:18.795Z (over 2 years ago)
- Topics: cresmd, deblurring, deep-learning, denoising, dynamic-networks, image-restoration, jpeg-deblocking, low-level-vision
- Language: Python
- Homepage: http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650052.pdf
- Size: 23.9 MB
- Stars: 90
- Watchers: 5
- Forks: 7
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# CResMD
### Interactive Multi-Dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration [Paper](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650052.pdf)
By Jingwen He*, [Chao Dong*](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ&hl=en), and [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/) (* indicates equal contribution)
two-dimension modulation
three-dimension modulation
Demo video of two-dimension modulation.
## Dependencies and Installation
- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux))
- [PyTorch >= 1.0](https://pytorch.org/)
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- 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](https://drive.google.com/drive/folders/1-ye2s6og03jHh5A0cjtINpOUickJEra0?usp=sharing)
1. Generate LQ images with different combinations of degradations using matlab [`codes/data_scripts/generate_2D_val.m`](codes/data_scripts/generate_2D_val.m), [`codes/data_scripts/generate_3D_val.m`](codes/data_scripts/generate_3D_val.m).
- **Modulation Testing**
1. (optional) Modify the configuration file [`options/test/modulation_CResMD.yml`](codes/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:
```c++
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`](codes/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:
```c++
python test.py -opt options/test/test_CResMD.yml
```
- **Test base network**
1. Modify the configuration file [`options/test/test_Base.yml`](codes/options/test/test_Base.yml).
1. Run command:
```c++
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`](codes/data).
1. Modify the configuration file [`options/train/train_CResMD.yml`](codes/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:
```c++
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`](codes/data).
1. Modify the configuration file [`options/train/train_Base.yml`](codes/options/train/train_Base.yml)
1. Run command:
```c++
python train.py -opt options/train/train_Base.yml
```
## Acknowledgement
- This code is based on [mmsr](https://github.com/open-mmlab/mmsr).