• Abstract
• News
• Method Overview
• Key Features
• Installation
• Usage
• Important Parameters
• Citation
• License

Effective denoising is crucial in low-dose CT to enhance subtle structures and low-contrast lesions while preventing diagnostic errors. Supervised methods struggle with limited paired datasets, and self-supervised approaches often require multiple noisy images and rely on deep networks like U-Net, offering little insight into the denoising mechanism. To address these challenges, we propose an interpretable self-supervised single-image denoising framework---Filter2Noise (F2N). Our approach introduces an Attention-Guided Bilateral Filter that adapted to each noisy input through a lightweight module that predicts spatially varying filter parameters, which can be visualized and adjusted post-training for user-controlled denoising in specific regions of interest. To enable single-image training, we introduce a novel downsampling shuffle strategy with a new self-supervised loss function that extends the concept of Noise2Noise to a single image and addresses spatially correlated noise. On the Mayo Clinic 2016 low-dose CT dataset, F2N outperforms the leading self-supervised single-image method (ZS-N2N) by 4.59 dB PSNR while improving transparency, user control, and parametric efficiency. These features provide key advantages for medical applications that require precise and interpretable noise reduction.

📢 2025-07: We have implemented a Mamba-based variant of Filter2Noise as an experimental alternative to the attention mechanism. However, preliminary results show that:

• More parameters: The Mamba implementation uses significantly more parameters (~8x increase) compared to the lightweight attention-based approach
• Worse performance: Initial testing indicates inferior denoising performance compared to the simple attention-based mechanism

Figure 1: (a) The F2N denoising pipeline. (b) The downsampling strategy, following ZS-N2N. (c) Our proposed Euclidean Local Shuffle (ELS).

• ✨ Interpretable Denoising: Visualizable filter parameters (σx, σy, σr) for understanding the denoising behavior.
• 🚀 Lightweight Architecture: Only 1.8k parameters for single-stage (F2N-S1) and 3.6k for two-stage (F2N-S2).
• 💡 Self-Supervised Learning: No clean reference images needed for training.
• 🖱️ User-Controlled Denoising: Post-training adjustment of filter parameters for region-specific denoising.

pip install -r requirements.txt

Important: Ensure your input CT image is normalized to the range [0, 1] before processing. Run the main script to apply the denoising pipeline:

python filter2noise.py

Launch the Gradio interface for interactive denoising and parameter adjustment:

python demo.py

The interactive demo allows you to:

• Upload and denoise your own CT images.
• Visualize the predicted filter parameters (σx, σy, σr).
• Adjust filter parameters in specific regions using interactive controls.
• See real-time updates of the denoised result.

The parameter λ in the loss function balances noise reduction (L_rec) and edge preservation (L_reg):

• Low λ: More aggressive noise reduction, potentially leading to blurring of fine details.
• High λ: Better preservation of edges and structures, but may leave more residual noise.
• Recommended Starting Value: λ=350. Adjust this value based on the specific image characteristics and desired denoising outcome.

• patch_size: Controls the granularity of adaptive filtering (default: 8). Smaller patches adapt more locally.
• num_stages: Number of Attention-Guided Bilateral Filter (AGBF) stages (1 or 2). More stages can improve results but increase computation.
• in_channels: Number of input image channels (default: 1 for grayscale CT images).

If you find this work useful in your research, please consider citing our paper:

@misc{sun2025filter2noise,
      title={Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral Filtering}, 
      author={Yipeng Sun and Linda-Sophie Schneider and Mingxuan Gu and Siyuan Mei and Chengze Ye and Fabian Wagner and Siming Bayer and Andreas Maier},
      year={2025},
      eprint={2504.13519},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2504.13519}, 
}

This project is open source and available under the Creative Commons Attribution-NonCommercial 4.0 International License. See the LICENSE file for more details.