U.S. Department of Energy

Pacific Northwest National Laboratory

Compressive Sensing in Microscopy at ASCI 2016

Abstract

Compressive Sensing in Microscopy: a Tutorial Andrew Stevens(1,2), Hao Yang(3), Libor Kovarik(1), Xin Yuan(4), Quentin Ramasse(5), Patricia Abellan(5), Yunchen Pu(2), Lawrence Carin(2), and Nigel D. Browning(1) 1. Pacific Northwest National Laboratory, Richland USA 2. Duke University, ECE, Durham USA 3. Lawrence Berkeley National Laboratory, Berkeley USA 4. Bell Laboratories, Murray Hill USA 5. SuperSTEM, STFC Daresbury Laboratories, Warrington UK Currently many types of microscopy are limited, in terms of spatial and temporal resolution, by hardware (e.g., camera framerate, data transfer rate, data storage capacity). The obvious approach to solve the resolution problem is to develop better hardware. An alternative solution, which additionally benefits from improved hardware, is to apply compressive sensing (CS) [1]. CS approaches have been shown to reduce dose by as much as 90% in electron microscopy [2, 3, 4]. Optical imaging and microscopy have also seen substantial benefits [5, 6, 7, 8, 9, 10, 11, 12, 13]. This tutorial will briefly introduce the principles of CS. Primarily, we will focus on the setup and modifications necessary for applying CS to a few different types of microscopy and spectroscopy Scanning microscopy (e.g., STEM [2], SEM, STXM) EELS, EDS, Mass spec TEM-video [3], optical-video [7] Phase-contrast imaging Tomography. We will show results for a few of these compressive sensing approaches approaches. Moreover, an approach for detecting CS reconstruction errors (i.e., errors introduced by the image processing algorithm) will be discussed.

Stevens AJ, H Yang, L Kovarik, X Yuan, QM Ramasse, P Abellan Baeza, Y Pu, L Carin, and ND Browning.  2016.  "Compressive Sensing in Microscopy: a Tutorial."  Abstract submitted to ASCI 2016, Boise, ID.  PNNL-SA-118175. 

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