非等方な解像度を有するFIB-SEMデータセットからの電極構造再構築

抄録

A framework for automated SOFC microstructure reconstruction from large asymmetric-resolution FIB-SEM datasets is proposed. Machine learning techniques are used for super-resolving the in-depth direction, i.e. FIB slicing direction, and for automating the phase segmentation. Deep neural networks consisting of patch-VDSR residual network for the increasing slicing resolution of FIB-SEM data and patch-CNN in the encoder-decoder configuration for semantic segmentation are incorporated. The proposed algorithm can shorten the FIB-SEM measurement time or increase the size of microstructures maintaining high resolution.