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详细说明:# DnCNN-tensorflow
[![AUR](https://img.shields.io/aur/license/yaourt.svg?style=plastic)](LICENSE)
[![Docker Automated build](https://img.shields.io/docker/automated/jrottenberg/ffmpeg.svg?style=plastic)](https://hub.docker.com/r/wenbodut/dncnn/)
[![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=plastic)](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
![graph](./img/model.png)
## Results
![compare](./img/compare.png)
- 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 coul
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