A posteriori correction of camera characteristics from large image data sets

Pavel Afanasyev, Raimond B. G. Ravelli, Rishi Matadeen, Sacha De Carlo, Gijs van Duinen, Bart Alewijnse, Peter Peters, Jan-Pieter Abrahams, Rodrigo V. Portugal, Michael Schatz, Marin van Heel*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Web of Science)

Abstract

Large datasets are emerging in many fields of image processing including: electron microscopy, light microscopy, medical X-ray imaging, astronomy, etc. Novel computer-controlled instrumentation facilitates the collection of very large datasets containing thousands of individual digital images. In single-particle cryogenic electron microscopy ("cryo-EM"), for example, large datasets are required for achieving quasi-atomic resolution structures of biological complexes. Based on the collected data alone, large datasets allow us to precisely determine the statistical properties of the imaging sensor on a pixel-by-pixel basis, independent of any "a priori" normalization routinely applied to the raw image data during collection ("flat field correction"). Our straightforward "a posteriori" correction yields clean linear images as can be verified by Fourier Ring Correlation (FRC), illustrating the statistical independence of the corrected images over all spatial frequencies. The image sensor characteristics can also be measured continuously and used for correcting upcoming images.
Original languageEnglish
Article number10317
JournalScientific Reports
Volume5
DOIs
Publication statusPublished - 11 Jun 2015

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