  Three-dimensional (3D) magnetic reconstruction is vital to the study of novel
magnetic materials for 3D spintronics. Vector field electron tomography (VFET)
is a major in house tool to achieve that. However, conventional VFET
reconstruction exhibits significant artefacts due to the unavoidable presence
of missing wedges. In this article, we propose a deep-learning enhanced VFET
method to address this issue. A magnetic textures library is built by
micromagnetic simulations. MagNet, an U-shaped convolutional neural network, is
trained and tested with dataset generated from the library. We demonstrate that
MagNet outperforms conventional VFET under missing wedge. Quality of
reconstructed magnetic induction fields is significantly improved.
