论文标题
清洁隐式3D结构来自嘈杂的2D词干图像
Clean Implicit 3D Structure from Noisy 2D STEM Images
论文作者
论文摘要
扫描透射电子显微镜（茎）在单个细胞成分的尺度上获取3D样品的2D图像。不幸的是，由于缺乏干净的噪声对，这些2D图像可能太嘈杂了，无法融合到有用的3D结构中，促进良好的Denoisers是具有挑战性的。另外，即使使用常规3D网格时，代表详细的3D结构也很难。在解决这两个局限性时，我们建议使用STEM的可区分图像形成模型，从而可以与STEM中2D传感器噪声的联合模型以及隐式3D模型一起学习。我们表明，这些模型的组合能够在没有监督和跑赢大盘的情况下成功地散布3D信号和噪声，同时又有几个基准。
Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D sample on the scale of individual cell components. Unfortunately, these 2D images can be too noisy to be fused into a useful 3D structure and facilitating good denoisers is challenging due to the lack of clean-noisy pairs. Additionally, representing a detailed 3D structure can be difficult even for clean data when using regular 3D grids. Addressing these two limitations, we suggest a differentiable image formation model for STEM, allowing to learn a joint model of 2D sensor noise in STEM together with an implicit 3D model. We show, that the combination of these models are able to successfully disentangle 3D signal and noise without supervision and outperform at the same time several baselines on synthetic and real data.
