发表机构
Yonsei University; Stanford University（延世大学; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低剂量CT去噪的传统模型未适配CT噪声特性的问题，提出ENCORE框架，含FlyingConv模块，实现去噪质量与效率提升，还可进行零样本条件去噪。
AI 中文摘要
尽管基于深度学习的去噪技术已广泛应用于低剂量CT领域，但传统模型采用为自然图像设计的通用架构，未能考虑CT噪声非平稳且空间相关的特性。为解决该问题，我们提出了高效噪声上下文感知表示（ENCORE）框架，该框架明确利用CT噪声特性与解剖特征。首先，我们基于超越传统高斯近似的真实噪声分布重新构建噪声合成流程，为训练对生成建立严谨基础。其次，我们提取局部噪声功率与相关上下文以指导去噪过程。为充分发挥噪声上下文的潜力，我们提出了FlyingConv模块，该模块可针对每个局部图像区域自适应调整卷积权重。值得注意的是，我们的方法在去噪质量与计算效率两方面均取得了显著提升。此外，在推理阶段操纵噪声上下文图的强度可实现零样本条件去噪，允许对输出图像纹理进行动态控制。整个流程可在指定网址获取。
英文摘要
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
