Trainable Joint Bilateral Filter Layer (PyTorch)

This repository implements a GPU-accelerated trainable joint bilateral filter layer (guidance image + three spatial and one range filter dimension) that can be directly included in any Pytorch graph, just as any conventional layer (FCL, CNN, ...). By calculating the analytical derivative of the joint bilateral filter with respect to its parameters, the guidance image, and the input, the (so far) hyperparameters can be automatically optimized via backpropagation for a calculated loss.

Our associated paper Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT can be found in Scientific Reports (open access) or on arXiv (pre-print).

Setup:

The C++/CUDA implemented forward and backward functions are compiled via the setup.py script using setuptools:

1. Create and activate a python environment (python>=3.7).
2. Install Torch (tested versions: 1.7.1, 1.9.0).
3. Install the joint bilateral filter layer via pip:

pip install jointbilateralfilter_torch

In case you encounter problems with 3. install the layer directly from our GitHub repository:

3. Download the repository.
4. Navigate into the extracted repo.
5. Compile/install the joint bilateral filter layer by calling

python setup.py install

Example scripts:

• Can be found in our GitHub repository
• Try out the forward pass by running the example_filter.py (requires Matplotlib and scikit-image).
• Run the gradcheck.py script to verify the correct gradient implementation.
• Run example_optimization.py to optimize the parameters of a joint bilateral filter layer to automatically denoise an image.

Optimized joint bilateral filter prediction:

Citation:

If you find our code useful, please cite our work

@article{wagner2022trainable,
  title={Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT},
  author={Wagner, Fabian and Thies, Mareike and Denzinger, Felix and Gu, Mingxuan and Patwari, Mayank and Ploner, Stefan and Maul, Noah and Pfaff, Laura and Huang, Yixing and Maier, Andreas},
  journal={Scientific Reports},
  volume={12},
  number={1},
  pages={1--9},
  year={2022},
  publisher={Nature Publishing Group},
  doi={https://doi.org/10.1038/s41598-022-22530-4}
 }

Implementation:

The general structure of the implementation follows the PyTorch documentation for creating custom C++ and CUDA extensions. The forward pass implementation of the layer is based on code from the Project MONAI framework, originally published under the Apache License, Version 2.0. The correct implementation of the analytical forward and backward pass can be verified by running the gradcheck.py script, comparing numerical gradients with the derived analytical gradient using the PyTorch built-in gradcheck function.

Troubleshooting

nvcc-related errors:

1. Compiling the filter layers requires the Nvidia CUDA toolkit. Check its version

   nvcc --version

   or install it via, e.g.,

   sudo apt update
   sudo apt install nvidia-cuda-toolkit

2. The NVIDIA CUDA toolkit 11.6 made some problems on a Windows machine in combination with pybind. Downgrading the toolkit to version 11.3 fixed the problem (see this discussion).

Windows-related problems:

1. Make sure the cl.exe environment variable is correctly set.