initial commit

CONTRIBUTING.md

+This repo is under development, your contributions are welcome.
+
+- If you have a question, feel free to open an issue.
+- If you want to contribue your code, fork it, and open your Pull Requests.

Dockerfile

+FROM tensorflow/tensorflow:1.4.1-gpu
+RUN pip install \
+    numpy
+VOLUME /workspace
+WORKDIR /workspace
+CMD python main.py --phase test --checkpoint_dir ./checkpoint_demo --test_set Set12

README.md

+# 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 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:
+
+
+![loss](./img/loss.png)
+
+## 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 ...
+
+
+
+
+
+

checkpoint_demo/checkpoint

+model_checkpoint_path: "DnCNN-tensorflow-67336"
+all_model_checkpoint_paths: "DnCNN-tensorflow-67336"