Noise2Noise

This is an unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration without Clean Data" [1].

There are several things different from the original paper (but not a fatal problem to confirm the noise2noise training framework):

• Training dataset (orignal: ImageNet, this repository: [2])
• Model (original: RED30 [3], this repository: SRResNet [4] or UNet)

Updates

• [Aug. 25, 2018] Add trained weights

Dependencies

• Keras, TensorFlow, NumPy, OpenCV

Train Noise2Noise

Download Dataset

mkdir dataset
cd dataset
wget https://cv.snu.ac.kr/research/VDSR/train_data.zip
wget https://cv.snu.ac.kr/research/VDSR/test_data.zip
unzip train_data.zip
unzip test_data.zip
cd ..

Train Model

Please see python3 train.py -h for optional arguments.

Train with Gaussian noise

# train model using (noise, noise) pairs (noise2noise)
python3 train.py --image_dir dataset/291 --test_dir dataset/Set14 --image_size 128 --batch_size 8 --lr 0.001 --output_path gaussian

# train model using (noise, clean) paris (standard training)
python3 train.py --image_dir dataset/291 --test_dir dataset/Set14 --image_size 128 --batch_size 8 --lr 0.001 --target_noise_model clean --output_path clean

Train with text insertion

# train model using (noise, noise) pairs (noise2noise)
python3 train.py --image_dir dataset/291 --test_dir dataset/Set14 --image_size 128 --batch_size 8 --lr 0.001 --source_noise_model text,0,50 --target_noise_model text,0,50 --val_noise_model text,25,25 --loss mae --output_path text_noise

# train model using (noise, clean) paris (standard training)
python3 train.py --image_dir dataset/291 --test_dir dataset/Set14 --image_size 128 --batch_size 8 --lr 0.001 --source_noise_model text,0,50 --target_noise_model clean --val_noise_model text,25,25 --loss mae --output_path text_clean

Model architectures

With --model unet, UNet model can be trained instead of SRResNet.

Noise Models

Using source_noise_model, target_noise_model, and val_noise_model arguments, arbitrary noise models can be set for source images, target images, and validatoin images respectively. Default values are taken from the experiment in [1].

• Gaussian noise
  • gaussian,min_stddev,max_stddev (e.g. gaussian,0,50)
• Clean target
  • clean
• Text insertion
  • text,min_occupancy,max_occupancy (e.g. text,0,50)

You can see how these noise models work by:

python3 noise_model.py --noise_model text,0,50

Results

Plot training history

python3 plot_history.py --input1 gaussian --input2 clean

Gaussian noise

From the above result, I confirm that we can train denoising model using noisy targets but it is not comparable to the model trained using clean targets.

Text insertion

Check denoising result

python3 test_model.py --weight_file [trained_model_path] --image_dir dataset/Set14

Gaussian noise

Denoising result by clean target model (left to right: original, degraded image, denoised image):

Denoising result by noise target model:

Text insertion

Denoising result by clean target model

Denoising result by noise target model:

Trained weights

Gaussian noise, noise target

Gaussian noise, clean target

Text insertion, noise target

Text insertion, clean target

TODOs

• Compare (noise, clean) training and (noise, noise) training
• Add different noise models
• Write readme

References

[1] J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, T. Aila, "Noise2Noise: Learning Image Restoration without Clean Data," in Proc. of ICML, 2018.

[2] J. Kim, J. K. Lee, and K. M. Lee, "Accurate Image Super-Resolution Using Very Deep Convolutional Networks," in Proc. of CVPR, 2016.

[3] X.-J. Mao, C. Shen, and Y.-B. Yang, "Image Restoration Using Convolutional Auto-Encoders with Symmetric Skip Connections," in Proc. of NIPS, 2016.

[4] C. Ledig, et al., "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network," in Proc. of CVPR, 2017.