Unsupervised Deep Image Prior for Sparse-View and Limited-Angle Electron Tomography
For materials scientists needing 3D characterization from limited electron tomography data, DIP offers an unsupervised alternative to supervised deep learning, reducing the need for training datasets.
Deep image prior (DIP) is applied to electron tomography under limited-angle and sparse-view conditions, achieving performance comparable to supervised methods on simulated data (e.g., tilt range 60°, increment 10°) and enabling reliable 3D quantification on experimental data.
Electron tomography (ET) plays an important role in the three-dimensional (3D) characterization of nanomaterials. However, under limited-angle and sparse-view conditions, conventional algorithms produce degraded reconstructions, which compromise the quality and interpretability of resulting 3D data. In this paper, we present deep image prior (DIP), an unsupervised deep learning (DL) approach, for highly degraded tomography acquisitions and demonstrate, using simulated data, that its performance is comparable to that of supervised approaches requiring training datasets, even for tilt ranges as limited as 60° and tilt increments of 10°. We then apply it to experimental data and show that it enables reliable 3D quantification under both sparse-view and limited-angle conditions, highlighting its potential for a wide range of materials and acquisition modalities.