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Super-compression of large electron microscopy time series by deep compressive sensing learning.

Siming Zheng1, Chunyang Wang1, Xin Yuan2

  • 1Department of Physics and Astronomy, University of California, Irvine, Irvine, CA, USA.

Patterns (New York, N.Y.)
|July 21, 2021
PubMed
Summary

Ultrafast electron microscopy (EM) generates big data. A new strategy combines deep learning and temporal compressive sensing (TCS) for efficient EM big data compression, enabling high-fidelity image reconstruction.

Keywords:
TEMbig datacompressioncompressive sensingdeep learningdirect detection devicedirect electron detectionelectron microscopyin situ

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Area of Science:

  • Materials Science
  • Data Science
  • Imaging Technology

Background:

  • Ultrafast detectors in electron microscopy (EM) enable nanomaterial dynamics studies.
  • The high data output from these detectors presents significant processing and storage challenges.

Purpose of the Study:

  • To develop a novel big data compression strategy for electron microscopy.
  • To address the challenges of processing and storing large datasets generated by ultrafast EM detectors.

Main Methods:

  • Combining deep learning with temporal compressive sensing (TCS) for data compression.
  • Utilizing TCS to compress sequential EM images into a single measurement.
  • Employing an end-to-end deep learning network for image reconstruction.

Main Results:

  • Achieved high-fidelity reconstruction of compressed videos with compression ratios up to 30.
  • Demonstrated superior compression efficiency and denoising capabilities compared to JPEG.
  • Significant savings in encoding power, memory, and transmission bandwidth.

Conclusions:

  • The proposed deep learning and TCS strategy offers an effective solution for EM big data compression.
  • This technique can be integrated with existing detectors, reducing hardware limitations.
  • Anticipated broad applications in edge computing for EM and other imaging modalities.