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

arXiv: https://arxiv.org/abs/2605.27139 · HTML: https://mlsanity.com/paper/2605.27139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 10 of 10)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Learned Primal-dual Reconstruction](https://mlsanity.com/paper/1707.06474/) (2017)
- [Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods](https://mlsanity.com/paper/2003.04989/) (2020)
- [Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction](https://mlsanity.com/paper/2405.02509/) (2024)
- [A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography](https://mlsanity.com/paper/2311.05539/) (2023)
- [Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction](https://mlsanity.com/paper/2405.03854/) (2024)
- [Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography](https://mlsanity.com/paper/2203.15413/) (2022)
- [Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization](https://mlsanity.com/paper/2312.05592/) (2023)
- [Robust plug-and-play methods for highly accelerated non-Cartesian MRI reconstruction](https://mlsanity.com/paper/2411.01955/) (2024)
- [Sampling Limits for Electron Tomography with Sparsity-exploiting Reconstructions](https://mlsanity.com/paper/1904.02614/) (2019)

## Abstract (arXiv)

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 60deg and tilt increments of 10deg. 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.