# DnCNN-tensorflow
[](LICENSE)
[](https://hub.docker.com/r/wenbodut/dncnn/)
[](CONTRIBUTING.md)
A tensorflow implement of the TIP2017 paper [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](http://www4.comp.polyu.edu.hk/~cslzhang/paper/DnCNN.pdf)
## Model Architecture
## Results
- BSD68 Average Result
The average PSNR(dB) results of different methods on the BSD68 dataset.
| Noise Level | BM3D | WNNM | EPLL | MLP | CSF |TNRD | DnCNN-S | DnCNN-B | DnCNN-tensorflow |
|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|
| 25 | 28.57 | 28.83 | 28.68 | 28.96 | 28.74 | 28.92 | **29.23** | **29.16** | **29.17** |
- Set12 Average Result
| Noise Level | DnCNN-S | DnCNN-tensorflow |
|:-----------:|:-------:|:----------------:|
| 25 | 30.44 | **30.38** |
For the dataset and denoised images, please download [here](https://drive.google.com/open?id=16x8E7h0srYQliXbrO0pvX6zogfW1hN2P)
## Environment
### :whale: With docker (recommended):
- Install docker support
You may do it like this(ubuntu):
``` shell
$ sudo apt-get install -y curl
$ curl -sSL https://get.docker.com/ | sh
$ sudo usermod -aG docker ${USER}
```
- Install nvidia-docker support(to make your GPU available to docker containers)
You may do it like this(ubuntu):
```shell
$ wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
$ sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
```
- Pull dncnn image and start a container
```shell
$ docker pull wenbodut/dncnn
$ ./rundocker.sh
```
Then you could train the model.
### Without docker:
You should make sure the following environment is contented
```
tensorflow == 1.4.1
numpy
```
## One-Key-To-Denoise
```
$ ./oneKeyToDenoise.sh
(need docker support)
```
Then you could find the noisy Set12 images and denoised images in test folder. Have fun!
## Train
```
$ python generate_patches.py
$ python main.py
(note: You can add command line arguments according to the source code, for example
$ python main.py --batch_size 64 )
```
For the provided model, it took about 4 hours in GTX 1080TI.
Here is my training loss:
**Note**: This loss figure isn't suitable for this trained model any more, but I don't want to update the figure :new_moon_with_face:
## Test
```
$ python main.py --phase test
```
## TODO
- [x] Fix bug #13. (bug #13 fixed, thanks to @sdlpkxd)
- [x] Clean source code. For instance, merge similar functions(e.g., 'load_images 'and 'load_image' in utils.py).
- [x] Add one-key denoising, with the help of docker.
- [x] Compare with original DnCNN.
- [x] Replace tf.nn with tf.layer.
- [ ] Replace PIL with OpenCV.
- [ ] Try tf.dataset API to speed up training process.
- [ ] Train a noise level blind model.
## Thanks for their contributions
- @lizhiyuanUSTC
- @husqin
- @sdlpkxd
- and so on ...
