DnCNN-tensorflow

a tensorflow implement of the TIP2017 paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Model Architecture

Results

Gaussian Denoising

GT

Noisy

Denoised

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	29.24

Environment

With docker (recommended):

0. Install docker, following the instructions on https://docs.docker.com/engine/installation/
1. Install nvidia-docker, to make your GPU available to docker containers
2. Create an image that can run the code from the provided Dockerfile. Call the image 'dncnn':

$ docker build --network host -t dncnn .
$ ./rundocker.sh

Then you could train the model.

Without docker:

You should make sure the following environment is contented

tensorflow = 1.0.1
numpy

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 )

Here is my training loss:

Test

$ python main.py --phase test

TODO

• Clean source code. For instance, merge similar functions(e.g., 'load_images 'and 'load_image' in utils.py)
• Replace PIL with OpenCV.
• Fix bugs, such as #13.
• Support the newest tensorflow(1.4)

Thanks for their contributions

• @lizhiyuanUSTC
• @husqin