发表机构
Nanjing University of Aeronautics and Astronautics; Charles Sturt University（南京航空航天大学; 查尔斯斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对电子显微镜成像分辨率与采集速度权衡问题，提出频率自适应双流架构，利用离散小波变换分解图像，通过条件扩散模型和变压器网络分别进行全局合成与细节恢复，实验表明该方法性能优越且泛化能力强。
AI 中文摘要
电子显微镜能够实现纳米级细胞可视化，但在成像分辨率和采集速度之间面临权衡。现有基于学习的方法依赖单流架构，难以平衡感知真实感和定量保真度，要么过度平滑细节，要么产生不现实的幻觉。本文引入频率自适应双流架构来解决这一冲突。利用离散小波变换将图像分解为低频结构和高频细节，然后采用条件扩散模型进行逼真的全局合成，采用变压器网络进行精确的细节恢复。在EMDiffuse数据集上的实验表明，该方法实现了卓越的LPIPS和分辨率比，显著优于现有方法。该方法在不同生物样本上也具有很强的泛化能力，支持用于结构生物学和纳米技术应用的快速可靠的电子显微镜成像。源代码和相关数据集已公开，以促进进一步研究。
英文摘要
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.
