BUIFD

This repository contains the neeeded files to reproduce the denoising results with the pretrained networks and to retrain and test models from the (TIP 2020) paper:

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

Contact author: Majed El Helou

The noise level learning and fusion techniques of our paper can be applied to different network architectures, and adapted to different problems. The example provided in this repository uses the DnCNN architecture, and is applied to additive Gaussian noise removal.

1. Dependencies

• PyTorch
• torchvision
• OpenCV for Python
• HDF5 for Python

2. Reproduce results with pretrained models

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The PyTorch implementation of DnCNN is based on that of the paper Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.

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The folder 'Inference_Pretrained' is sufficient on its own to re-run the paper's experiments and to visualize results. It contains all 8 models used in the paper, under 'Pretrained_Models', and the testing data under 'Data'. To re-run all denoising experiments you can run:

bash inference_calls

or select a single experiment, for instance:

python test.py --color_mode gray --model R --max_train_noise 55 --epoch 49 --varying_noise True

which runs grayscale image denoising of the BSD68 test set with the DnCNN model (R for regular, F for BUIFD) that was trained up to noise level 55, and trained for 50 epochs. The test is carried out with varying noise in this case, with all the noise levels of the paper. Results are saved in 'Logs' (already available in this repository too), and can be visualized by running:

jupyter notebook visualize_PSNR_results.ipynb

Note: the notebook assumes all experimental results are already generated, which is the case when you download the repository.

3. Re-train networks

The folder 'Training' is sufficient on its own to re-train any of the network models. It contains the 'training_data' and 'testing_data', the models are saved for each epoch in a subdirectory inside 'saved_models' and test results are saved in 'Logs'. Average PSNR test results per noise level are also printed at the end of training. To re-train all 8 models with default settings, you can run:

bash example_train_test

Or you can train custom models individually:

python train.py --net_mode F --noise_max 55 --color 1 --preprocess True

which trains a BUIFD model with maximum training noise level 55. Then evaluate it on the CBSD68 test set:

python test.py --color_mode color --model F --max_train_noise 55 --epoch 49

Note: You can set 'preprocess' to False (default setting) if it is not the first time you train a certain model, and the hdf5 file is already generated and saved in the code directory. Otherwise it needs to be set to True.

4. BM3D comparison

For comparisons with BM3D and CBM3D denoising, the authors' code can be found on this link.

Citation

@article{elhelou2020blind,
    title   = {Blind universal {Bayesian} image denoising with {Gaussian} noise level learning},
    author  = {El Helou, Majed and S{\"u}sstrunk, Sabine},
    journal = {IEEE Transactions on Image Processing},
    volume  = {29},
    pages   = {4885--4897},
    year    = {2020}
}