统计特征引导的高分辨透射电子显微镜快速成像去噪
Statistical Characteristic-Guided Denoising for Rapid High-Resolution Transmission Electron Microscopy Imaging
把统计特征（空间偏差、频带能量）作为权重来调制卷积核与频域增益，是一种可迁移到其他强噪声成像域（低剂量 CT、荧光显微、天文成像）的去噪先验注入方式；其针对特定成像模态的噪声标定与仿真数据生成流程也值得在数据稀缺的科学成像任务中复用。
原文依据 we propose a statistical characteristic-guided denoising network, which utilizes statistical characteristics to guide the denoising process in both spatial and frequency domains
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论高分辨透射电子显微镜（HRTEM）能够实现成核动力学的原子尺度观测，从而推动先进固体材料的研究。然而，由于成核过程在毫秒尺度上快速变化，需要短曝光快速成像，这会带来严重噪声，掩盖原子位置。在本工作中，我们提出一种统计特征引导的去噪网络，利用统计特征在空间域和频率域共同引导去噪过程。在空间域，我们提出空间偏差引导加权，依据偏差特征为每个空间位置选择合适的卷积操作。在频率域，我们提出频带引导加权，依据频带特征增强信号并抑制噪声。我们还开发了一种面向 HRTEM 的噪声标定方法，并生成了包含无序结构和真实 HRTEM 图像噪声的数据集，从而保证模型在真实图像上用于成核观测时的去噪性能。在合成数据与真实数据上的实验表明，我们的方法在 HRTEM 图像去噪上优于最优方法，并在定位下游任务中有效。代码将发布于 https://github.com/HeasonLee/SCGN。
英文原文摘要 1315 字符
High-Resolution Transmission Electron Microscopy (HRTEM) enables atomic-scale observation of nucleation dynamics, which boosts the studies of advanced solid materials. Nonetheless, due to the millisecond-scale rapid change of nucleation, it requires short-exposure rapid imaging, leading to severe noise that obscures atomic positions. In this work, we propose a statistical characteristic-guided denoising network, which utilizes statistical characteristics to guide the denoising process in both spatial and frequency domains. In the spatial domain, we present spatial deviation-guided weighting to select appropriate convolution operations for each spatial position based on deviation characteristic. In the frequency domain, we present frequency band-guided weighting to enhance signals and suppress noise based on band characteristics. We also develop an HRTEM-specific noise calibration method and generate a dataset with disordered structures and realistic HRTEM image noises. It can ensure the denoising performance of models on real images for nucleation observation. Experiments on synthetic and real data show our method outperforms the state-of-the-art methods in HRTEM image denoising, with effectiveness in the localization downstream task. Code will be available at https://github.com/HeasonLee/SCGN.