Abstract
Achievement of an optimal improvement in signal-to-noise ratio from image averaging techniques depends crucially on the assumption that all members of the set of images to be averaged are fundamentally alike. In HREM of biological macromolecules, this assumption may be invalid for such reasons as variations in viewing geometry, non-uniformity of staining, or structural perturbations caused by specimen preparation procedures or radiation damage. Inclusion of data that are compromized by these or other factors will degrade the information content of the averaged image. Here we present an algorithm which provides an objective quantitative method for the identification and elimination of anomalous members of a set of pre-aligned images. Based on a statistical criterion of mutual consistency, the algorithm forms an ordered list in which the individual images are ranked from most to least reliable. On specification of the noise statistics - in the formulation given here, of stationary white noise — an acceptability threshold in this ordered list is imposed. The derivation and implementation of this algorithm are presented, its properties discussed, and its application illustrated using both real and model electron micrograph data.
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