Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
Suhas Sreehari, S. V. Venkatakrishnan, Brendt Wohlberg, Lawrence F., Drummy, Jeffrey P. Simmons, Charles A. Bouman

TL;DR
This paper introduces a plug-and-play prior framework for electron tomography and sparse image interpolation that leverages non-local redundancy and modern denoising algorithms to improve reconstruction quality and convergence.
Contribution
It adapts the plug-and-play priors framework to electron tomography, providing a flexible method that incorporates advanced denoising algorithms and guarantees convergence.
Findings
Higher quality reconstructions on simulated data
Improved convergence over existing methods
Effective use of non-local means denoising
Abstract
Many material and biological samples in scientific imaging are characterized by non-local repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a 2D image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the non-local redundancy in images. We adapt a framework, termed plug-and-play (P&P) priors, to solve these imaging problems in a regularized inversion setting. The power of the P&P approach is that it allows a wide array of modern denoising algorithms to be used as a "prior model" for tomography and image…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
