Gathering 3D material microstructural information is time-consuming, expensive, and energy-intensive. Acquisition of 3D data has been accelerated by developments in serial sectioning instrument capabilities; however, for crystallographic information, the electron backscatter diffraction (EBSD) imaging modality remains rate limiting. We propose a physics-based efficient deep learning framework to reduce the time and cost of collecting 3D EBSD maps. Our framework uses a quaternion residual block self-attention network (QRBSA) to generate high-resolution 3D EBSD maps from sparsely sectioned EBSD maps. In QRBSA, quaternion-valued convolution effectively learns local relations in orientation space, while self-attention in the quaternion domain captures long-range correlations. We apply our framework to 3D data collected from commercially relevant titanium alloys, showing both qualitatively and quantitatively that our method can predict missing samples (EBSD information between sparsely sectioned mapping points) as compared to high-resolution ground truth 3D EBSD maps.

翻译：获取三维材料微观结构信息耗时、昂贵且能耗高。串行切片仪器能力的进步加速了三维数据的采集，但对于晶体学信息而言，电子背散射衍射（EBSD）成像模式仍是限制速度的瓶颈。我们提出一种基于物理的高效深度学习框架，以减少采集三维EBSD地图的时间和成本。该框架采用四元数残差块自注意力网络（QRBSA），从稀疏切片EBSD地图生成高分辨率三维EBSD地图。在QRBSA中，四元数值卷积有效学习取向空间中的局部关系，而四元数域的自注意力则捕获长程相关性。我们将该框架应用于商业相关钛合金采集的三维数据，定性和定量结果表明，与高分辨率真实三维EBSD地图相比，我们的方法能够预测缺失样本（稀疏切片映射点之间的EBSD信息）。