AI RESEARCH
Unsupervised Deep Image Prior for Sparse-View and Limited-Angle Electron Tomography
arXiv CS.CV
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ArXi:2605.27139v1 Announce Type: cross 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 nstrate, using simulated data, that its performance is comparable to that of supervised approaches requiring