Abstract
Low-dose computed tomography (LDCT) reduces radiation exposure but also introduces substantial noise and structural degradation, making it difficult to suppress noise without erasing subtle anatomical details. In this paper, we present TAFG-MAN, a latent diffusion framework for efficient and high-quality LDCT image denoising. The framework combines a perceptually optimized autoencoder, conditional latent diffusion restoration in a compact latent space, and a lightweight Timestep-Adaptive Frequency-Gated (TAFG) conditioning design. TAFG decomposes condition features into low- and high-frequency components, predicts timestep-adaptive gates from the current denoising feature and timestep embedding, and progressively releases high-frequency guidance in later denoising stages before cross-attention. In this way, the model relies more on stable structural guidance at early reverse steps and introduces fine details more cautiously as denoising proceeds, improving the balance between noise suppression and detail preservation. Experiments show that TAFG-MAN achieves a favorable quality-efficiency trade-off against representative baselines. Compared with its base variant without TAFG, it further improves detail preservation and perceptual quality while maintaining essentially the same inference cost, and ablation results confirm the effectiveness of the proposed conditioning mechanism.
Abstract (translated)
低剂量计算机断层扫描（LDCT）虽能减少辐射暴露，但也会引入大量噪声和结构退化，使得在抑制噪声的同时保留细微解剖结构变得困难。本文提出TAFG-MAN，一种用于高效高质量LDCT图像去噪的潜在扩散框架。该框架结合了感知优化自编码器、紧凑潜在空间中的条件潜在扩散复原，以及轻量级的时间步自适应频率门控（TAFG）条件设计。TAFG将条件特征分解为低频与高频分量，从当前去噪特征和时间步嵌入中预测时间步自适应门控，并在交叉注意力之前于后续去噪阶段逐步释放高频引导。如此，模型在早期反向步骤中更依赖稳定的结构引导，并在去噪过程中更谨慎地引入精细细节，从而改善噪声抑制与细节保留之间的平衡。实验表明，TAFG-MAN相较于代表性基线方法实现了质量与效率的有利权衡。与不含TAFG的基础变体相比，其在保持推理成本基本不变的同时进一步提升了细节保留和感知质量，消融实验结果也验证了所提条件机制的有效性。
URL
https://arxiv.org/abs/2603.20868
