Multi-scale Loss based Electron Microscopic Image Pair Matching Method

Nanodiffraction Imaging (NDI), a novel imaging technique based on the scanning transmission electron mi-croscopy (STEM), helps the understanding of the relationships between micro-structure and macro-properties. However, the analysis requires image pair matching tasks through meticulous and time-consuming observation and selection by domain experts. Therefore, this paper proposes an image pair matching method for NDI images. The proposed method adopts a two-step training approach. The first step is to perform pre-training by contrastive learning on specialized NDI images. The second step is fine-tuning by a customized model on image pair matching tasks. In the second step, the training is performed with the incorporation of a special loss function, OriDist loss. This loss function is designed to focus on orientation and distribution of multi-scale features. The evaluation results demonstrate the ability of the proposed method to achieve high-accuracy NDI image pair matching, and efficiently reduce the search space of candidate matching images, resulting in a significant reduction in the human workload. Through an extensive ablation study, each component of the proposed method shows positive contributions to the overall performance.