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Biological samples, and especially samples embedded in thin [[vitreous ice]], are highly radiation sensitive, thus only low electron doses can be used to image the sample. This low dose, as well as variations in the [https://www.leica-microsystems.com/science-lab/brief-introduction-to-contrasting-for-em-sample-preparation/ metal stain] used (if used) means images have high noise relative to the signal given by the particle being observed. By aligning several similar images to each other so they are in register and then averaging them, an image with higher [[signal-to-noise ratio]] can be obtained. As the noise is mostly randomly distributed and the underlying image features constant, by averaging the intensity of each pixel over several images only the constant features are reinforced. Typically, the optimal alignment (a [[Translation (geometry)|translation]] and an in-plane rotation) to map one image onto another is calculated by [[cross-correlation]].
However, a micrograph often contains particles in multiple different orientations and/or conformations, and so to get more representative image averages, a method is required to group similar particle images together into multiple sets. This is normally carried out using one of several data analysis and image classification algorithms, such as [[Multivariate statistics|multi-variate statistical analysis]] and hierarchical ascendant classification, or [[k-means clustering|''k''-means clustering]].{{cn}}
Often data sets of tens of thousands of particle images are used, and to reach an optimal solution an [[Iteration|iterative]] procedure of alignment and classification is used, whereby strong image averages produced by classification are used as reference images for a subsequent alignment of the whole data set.
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