![[2504.13519] Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2504.13519] Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising
Summary
The paper presents Filter2Noise, a novel framework for interpretable and zero-shot low-dose CT image denoising, achieving state-of-the-art results with minimal parameters.
Why It Matters
This research addresses a significant challenge in medical imaging, where noise in low-dose CT scans can hinder diagnosis. By offering a transparent and efficient denoising method, Filter2Noise enhances the reliability of CT imaging, which is crucial for clinical applications and patient care.
Key Takeaways
- Filter2Noise utilizes an Attention-Guided Bilateral Filter for transparent denoising.
- Achieves state-of-the-art performance with only 3.6k parameters, enhancing efficiency.
- Introduces a multi-scale self-supervised loss to improve denoising from single images.
- Validates effectiveness on clinical photon-counting CT data, expanding its applicability.
- Combines high performance with user control, addressing trust issues in AI-based medical imaging.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2504.13519 (eess) [Submitted on 18 Apr 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising Authors:Yipeng Sun, Linda-Sophie Schneider, Siyuan Mei, Jinhua Wang, Ge Hu, Mingxuan Gu, Chengze Ye, Fabian Wagner, Lan Song, Siming Bayer, Andreas Maier View a PDF of the paper titled Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising, by Yipeng Sun and 10 other authors View PDF HTML (experimental) Abstract:Noise in low-dose computed tomography (LDCT) can obscure important diagnostic details. While deep learning offers powerful denoising, supervised methods require impractical paired data, and self-supervised alternatives often use opaque, parameter-heavy networks that limit clinical trust. We propose Filter2Noise (F2N), a novel self-supervised framework for interpretable, zero-shot denoising from a single LDCT image. Instead of a black-box network, its core is an Attention-Guided Bilateral Filter, a transparent, content-aware mathematical operator. A lightweight attention module predicts spatially varying filter parameters, making the process transparent and allowing interactive radiologist control. To learn from a single image with correlated noise, we introduce a multi-scale self-supervised loss coupled with Euclidean Local Shuffle (ELS) to disrupt noise patterns while prese...
