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# 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 ...