Trainable Bilateral Filter Layer (PyTorch)

Last update: Dec 25, 2022

Overview

Trainable Bilateral Filter Layer (PyTorch)

This repository contains our GPU-accelerated trainable bilateral filter layer (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 bilateral filter with respect to its parameters and its input, the (so far) hyperparameters can be automatically optimized via backpropagation for a calculated loss.

Our corresponding paper Ultra low-parameter denoising: Trainable bilateral filter layers in computed tomography can be found on arXiv.

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.

Setup:

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

Create and activate a python environment (python>=3.7).
Install Torch (tested versions: 1.7.1, 1.9.0).
Navigate into the extracted repo.
Compile/install the bilateral filter layer by calling

python setup.py install

Example scripts:

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 bilateral filter layer to automatically denoise an image.

Optimized bilateral filter prediction:

Citation:

If you find our code useful, please cite our work

@article{wagner2022ultra,
  title={Ultra low-parameter denoising: Trainable bilateral filter layers in computed tomography},
  author={Wagner, Fabian and Thies, Mareike and Gu, Mingxuan and Huang, Yixing and Pechmann, Sabrina and Patwari, Mayank and Ploner, Stefan and Aust, Oliver and Uderhardt, Stefan and Schett, Georg and Christiansen, Silke and Maier, Andreas},
  journal={arXiv preprint arXiv:2201.10345},
  year={2022}
}

You might also like...

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

HaloNet - Pytorch Implementation of the Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones. This re

189 Nov 22, 2022

Comments

3D case example

Hi, I am grateful that you have shared such awesome codes. I have downloaded the codes and tested 2D case. It works well. But when I used 3D images, the results seemed weird. As shown below.

The input 3D image is like this:

Could you provide 3D demo case? Thank you very much!

opened by cs123951 2

Trainable Bilateral Filter Layer (PyTorch)

Related tags

Overview

Trainable Bilateral Filter Layer (PyTorch)

Implementation:

Setup:

Example scripts:

Optimized bilateral filter prediction:

Citation:

You might also like...

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

Pytorch Implementation of Continual Learning With Filter Atom Swapping (ICLR'22 Spolight) Paper

Predictive AI layer for existing databases.

Predictive AI layer for existing databases.

An abstraction layer for mathematical optimization solvers.

Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021)

Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

Comments

3D case example

Releases(1.1.0)

1.1.0(Aug 1, 2022)

Owner

FabianWagner

Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)

Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19 (Oral).

dualFace: Two-Stage Drawing Guidance for Freehand Portrait Sketching (CVMJ)

Laser device for neutralizing - mosquitoes, weeds and pests

This is a simple backtesting framework to help you test your crypto currency trading. It includes a way to download and store historical crypto data and to execute a trading strategy.

All supplementary material used by me while TA-ing CS3244: Machine Learning

some academic posters as references. May we have in-person poster session soon!

Deep Reinforcement Learning based Trading Agent for Bitcoin

The code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention.

A complete speech segmentation system using Kaldi and x-vectors for voice activity detection (VAD) and speaker diarisation.

DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting

Code for "AutoMTL: A Programming Framework for Automated Multi-Task Learning"

A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

Customer-Transaction-Analysis - This analysis is based on a synthesised transaction dataset containing 3 months worth of transactions for 100 hypothetical customers.

Mask-invariant Face Recognition through Template-level Knowledge Distillation

Dense Prediction Transformers

The 2nd Version Of Slothybot

Fast (simple) spectral synthesis and emission-line fitting of DESI spectra.

Convnet transfer - Code for paper How transferable are features in deep neural networks?

Official implementation of NeurIPS'2021 paper TransformerFusion