Unsupervised Deep Image Prior for Sparse-View and Limited-Angle Electron Tomography

2026-05-26

AI Summary

This work addresses the severe degradation in reconstruction quality in electron tomography under sparse and limited-angle acquisition by introducing, for the first time, unsupervised deep image prior (DIP) into this domain. By integrating the physical imaging model into an end-to-end optimization framework, the method achieves high-quality three-dimensional reconstructions without requiring any training data. Evaluated under extreme conditions—limited to a 60° tilt range with 10° tilt increments—the approach demonstrates reconstruction performance on both simulated and experimental datasets that rivals supervised learning methods. It substantially enhances structural fidelity and quantitative reliability in low-sampling scenarios, establishing a new paradigm for unsupervised learning in electron tomography.

Abstract

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.

Problem

• electron tomography
• limited-angle
• sparse-view
• 3D reconstruction
• image degradation

Innovation

• Deep Image Prior
• Unsupervised Deep Learning
• Electron Tomography
• Sparse-View Reconstruction
• Limited-Angle Tomography