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.
Highlights
When thousands or tens-of-thousands of images are to be studied extensively by advanced averaging algorithms, a simple a priori correction can prove insufficient
In cryo-EM, the calibration of the a priori correction must be repeated regularly and performed under approximately the same conditions used in the subsequent data collection, since the pixel properties may change with the average exposure level[5]
When a large digital image dataset is available, collected with the same image transducer, that dataset itself can be used for the statistical characterization of every pixel in the sensor
Summary
When thousands or tens-of-thousands of images are to be studied extensively by advanced averaging algorithms, a simple a priori correction can prove insufficient. The development of advanced digital cameras has been instrumental in pushing the resolution attainable by single-particle cryo-EM towards near-atomic levels[3,4,5,6,7,8]. This approach requires the use of large datasets to bring the noisy, low-contrast image information in the micrographs to statistical significance through extensive averaging procedures. In cryo-EM, the calibration of the a priori correction must be repeated regularly and performed under approximately the same conditions used in the subsequent data collection, since the pixel properties may change with the average exposure level[5]. We characterize each pixel in terms of the average density and standard deviation of its pixel vector and exploit that information to normalize its output
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