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 preserving anatomical integrity. On the Mayo Clinic LDCT Challenge, F2N achieves state-of-the-art results, outperforming competing zero-shot methods by up to 3.68 dB in PSNR. It accomplishes this with only 3.6k parameters, orders of magnitude fewer than competing models, which accelerates inference and simplifies deployment. By combining high performance with transparency, user control, and high parameter efficiency, F2N offers a trustworthy solution for LDCT enhancement. We further demonstrate its applicability by validating it on clinical photon-counting CT data. Code is available at: https://github.com/sypsyp97/Filter2Noise.

翻译：低剂量计算机断层扫描（LDCT）中的噪声会掩盖重要的诊断细节。尽管深度学习提供了强大的去噪能力，但有监督方法需要不切实际的配对数据，而无监督的替代方案通常使用不透明且参数繁多的网络，限制了临床信任度。我们提出了Filter2Noise（F2N），这是一种新颖的自监督框架，能够从单幅LDCT图像实现可解释的零样本去噪。其核心并非黑盒网络，而是一个注意力引导双边滤波器——一种透明、内容感知的数学算子。一个轻量级的注意力模块预测空间变化的滤波器参数，使得整个过程透明化，并允许放射科医生进行交互控制。为了从具有相关噪声的单幅图像中学习，我们引入了一种多尺度自监督损失，并结合欧几里得局部洗牌（ELS）来破坏噪声模式，同时保持解剖结构的完整性。在梅奥诊所LDCT挑战赛上，F2N取得了最先进的结果，在PSNR指标上优于其他零样本方法高达3.68 dB。它仅用3.6k参数就实现了这一目标，比竞争模型少了数个数量级，从而加速了推理并简化了部署。通过将高性能与透明度、用户控制和高参数效率相结合，F2N为LDCT增强提供了一个可信赖的解决方案。我们通过在临床光子计数CT数据上进行验证，进一步证明了其适用性。代码发布于：https://github.com/sypsyp97/Filter2Noise。