针对低剂量CT噪声特性，提出高效上下文感知去噪框架
ENCORE: Efficient Noise Context-Aware Representation for Low-Dose CT Denoising
- 基于真实噪声分布重构数据生成，提升训练合理性
- 引入局部噪声功率与相关性上下文，显著改善去噪质量
- 支持推理时动态调节纹理，适用于临床个性化需求
尽管基于深度学习的去噪已广泛应用于低剂量CT，但传统模型采用自然图像通用架构，未能充分考虑CT噪声非平稳且空间相关的特性。为此，我们提出一种高效噪声上下文感知表示框架ENCORE，显式利用CT噪声特性和解剖结构特征。首先，基于超越传统高斯近似的现实噪声分布重构噪声合成过程，为配对数据生成建立严谨基础；其次，提取局部噪声功率与相关性上下文以指导去噪。为充分挖掘噪声上下文潜力，提出FlyingConv模块，可自适应调整每个局部图像区域的卷积权重。所提方法在去噪质量与计算效率上均有显著提升。此外，推理时调节噪声上下文图强度即可实现零样本条件去噪，动态控制输出图像纹理。完整代码开源：https://github.com/minwoo-yu/ENCORE.git
原文摘要 · Abstract (English)
While deep learning-based denoising has become widely adopted in low-dose CT, conventional models use generic architectures designed for natural images, failing to account for non-stationary and spatially correlated CT noise characteristics. To address this, we propose an Efficient Noise COntext-aware REpresentation (ENCORE) framework that explicitly leverages CT noise characteristics and anatomical features. First, we reformulate the noise synthesis procedure based on a realistic noise distribution beyond the conventional Gaussian approximation, establishing a rigorous foundation for training pair generation. Next, we extract local noise power and correlation contexts to guide the denoising process. To fully leverage the potential of noise context, we propose a FlyingConv module, which adaptively changes convolution weights for each local image region. Notably, our approach demonstrates substantial gains in both denoising quality and computational efficiency. Furthermore, manipulating the intensity of the noise context maps at inference time enables zero-shot conditional denoising, allowing for dynamic control over the output image texture. The entire pipeline is available at https://github.com/minwoo-yu/ENCORE.git
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