Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for
  Low-Dose CT with Attention-Guided Bilateral Filtering
Authors: name: Yipeng Sun, name: Linda-Sophie Schneider, name: Mingxuan Gu, name: Siyuan Mei, name: Chengze Ye, name: Fabian Wagner, name: Siming Bayer, name: Andreas Maier

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 .