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As an emerging technology, cryo-electron microscopy (cryo-EM) has attracted more and more research interests from both structural biology and computer science, because many challenging computational tasks are involved in the processing of cryo-EM images. An important image processing step is to cluster the 2D cryo-EM images according to their projection angles, then the cluster mean images are used for the subsequent 3D reconstruction. However, cryo-EM images are quite noisy and denoising them is not easy, because the noise is a complicated mixture from samples and hardware. In this study, we design an effective cryo-EM image denoising model, CDAE, i.e., a cascade of denoising autoencoders. The new model comprises stacked blocks of deep neural networks to reduce noise in a progressive manner. Each block contains a convolutional autoencoder, pre-trained by simulated data of different SNRs and fine-tuned by target data set. We assess this new model on three simulated test sets and a real data set. CDAE achieves very competitive PSNR (peak signal-to-noise ratio) in the comparison of the state-of-the-art image denoising methods. Moreover, the denoised images have significantly enhanced clustering results compared to original image features or high-level abstraction features obtained by other deep neural networks. Both quantitative and visualized results demonstrate the good performance of CDAE for the noise reduction in clustering single-particle cryo-EM images.

CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images