Filter2Noise：基於注意力引導雙邊濾波的低劑量CT單圖像去噪之可解釋自監督學習

Filter2Noise: Interpretable Self-Supervised Single-Image Denoising for Low-Dose CT with Attention-Guided Bilateral Filtering

April 18, 2025

作者: Yipeng Sun, Linda-Sophie Schneider, Mingxuan Gu, Siyuan Mei, Chengze Ye, Fabian Wagner, Siming Bayer, Andreas Maier

cs.AI

摘要

在低劑量CT中，有效的去噪對於增強細微結構和低對比度病變至關重要，同時防止診斷錯誤。監督方法受限於配對數據集的稀缺，而自監督方法通常需要多張噪聲圖像並依賴於如U-Net等深度網絡，對去噪機制的解釋性較弱。為應對這些挑戰，我們提出了一種可解釋的自監督單圖像去噪框架——Filter2Noise（F2N）。我們的方法引入了一種注意力引導的雙邊濾波器，該濾波器通過一個輕量級模塊適應每個噪聲輸入，預測空間變化的濾波參數，這些參數可在訓練後可視化並調整，實現用戶對特定感興趣區域的去噪控制。為了實現單圖像訓練，我們提出了一種新穎的下採樣重排策略，並配合新的自監督損失函數，將Noise2Noise的概念擴展到單圖像，並解決空間相關噪聲問題。在Mayo Clinic 2016低劑量CT數據集上，F2N在PSNR指標上超越了領先的自監督單圖像方法（ZS-N2N）4.59 dB，同時提升了透明度、用戶控制能力和參數效率。這些特性為需要精確且可解釋去噪的醫療應用提供了關鍵優勢。我們的代碼展示於https://github.com/sypsyp97/Filter2Noise.git。

English

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 .