title: Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for
    Low-Dose CT with Attention-Guided Bilateral Filtering
summary: 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. Our code is demonstrated at
  https://github.com/sypsyp97/Filter2Noise.git .
paper_authors._id: 68066bce27a3e396583a2b13
paper_authors.name: Yipeng Sun
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-04-21T16:01:32.331Z
paper_authors._id: 6423b89623b98d5ee6ebbae4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6423b89623b98d5ee6ebbae4/WiotUlYYZpVM9aH5sFAst.jpeg
paper_authors.fullname: Yipeng Sun
paper_authors.type: user
paper_authors.user: yipengsun
paper_authors._id: 68066bce27a3e396583a2b14
paper_authors.name: Linda-Sophie Schneider
paper_authors._id: 68066bce27a3e396583a2b15
paper_authors.name: Mingxuan Gu
paper_authors._id: 68066bce27a3e396583a2b16
paper_authors.name: Siyuan Mei
paper_authors._id: 68066bce27a3e396583a2b17
paper_authors.name: Chengze Ye
paper_authors._id: 68066bce27a3e396583a2b18
paper_authors.name: Fabian Wagner
paper_authors._id: 68066bce27a3e396583a2b19
paper_authors.name: Siming Bayer
paper_authors._id: 68066bce27a3e396583a2b1a
paper_authors.name: Andreas Maier
paper_title: Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for
    Low-Dose CT with Attention-Guided Bilateral Filtering
paper_summary: 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. Our code is demonstrated at
  https://github.com/sypsyp97/Filter2Noise.git .
paper_ai_summary: Filter2Noise uses an Attention-Guided Bilateral Filter and a novel downsampling shuffle strategy for interpretable single-image denoising in low-dose CT.
paper_ai_keywords: Attention-Guided Bilateral Filter single-image denoising Noise2Noise ZS-N2N downsampling shuffle strategy self-supervised loss function low-dose CT PSNR