  Different tasks in the computational pipeline of single-particle
cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly
noisy raw images. To this end, we develop an efficient algorithm for signal
enhancement of cryo-EM images. The enhanced images can be used for a variety of
downstream tasks, such as 2-D classification, removing uninformative images,
constructing {ab initio} models, generating templates for particle picking,
providing a quick assessment of the data set, dimensionality reduction, and
symmetry detection. The algorithm includes built-in quality measures to assess
its performance and alleviate the risk of model bias. We demonstrate the
effectiveness of the proposed algorithm on several experimental data sets. In
particular, we show that the quality of the resulting images is high enough to
produce ab initio models of $\sim 10$ \AA resolution. The algorithm is
accompanied by a publicly available, documented and easy-to-use code.
