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# 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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