通过分频双流架构，同时提升电镜图像的细节真实感与准确性。
Frequency-Aware Dual-Stream Learning for Balanced Realism and Fidelity in Electron Microscopy Imaging
- 将图像分解为低频结构和高频细节，分别用扩散模型和Transformer处理
- 在EMDiffuse数据集上实现更优的LPIPS与分辨率比，优于现有方法
- 适合需要快速高保真电镜成像的结构生物学与纳米技术研究
电子显微镜可实现纳米级细胞成像，但存在分辨率与采集速度之间的权衡。现有基于学习的方法依赖单流架构，在感知真实感与定量保真度之间难以兼顾，或过度平滑细节，或生成不真实的幻觉。本文提出一种频率自适应双流架构，利用离散小波变换将图像分解为低频结构与高频细节，分别采用条件扩散模型进行全局真实感合成，以及使用Transformer网络实现精确细节恢复。在EMDiffuse数据集上的实验表明，该方法在LPIPS与分辨率比指标上均显著优于现有方法。同时具备跨多种生物样本的强泛化能力，支持结构生物学与纳米技术领域的快速可靠电镜成像。源代码与相关数据集已公开，便于后续研究。
原文摘要 · Abstract (English)
Electron microscopy enables nanoscale cellular visualization but faces a trade-off between imaging resolution and acquisition speed. Existing learning-based methods rely on single-stream architectures that struggle to balance perceptual realism and quantitative fidelity, either over-smoothing details or generating unrealistic hallucinations. This work introduces a frequency-adaptive dual-stream architecture to resolve this conflict. Using discrete wavelet transform, we decompose images into low-frequency structures and high-frequency details, then employ a conditional diffusion model for realistic global synthesis and a transformer network for precise detail recovery. Experiments on the EMDiffuse dataset show the method achieves superior LPIPS and resolution ratio, substantially outperforming existing approaches. The method also shows strong generalization across diverse biological samples, supporting fast and reliable electron microscopy imaging for structural biology and nanotechnology applications. The source code and associated dataset are publicly available to facilitate further research.
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