  The cryo-electron microscopy (Cryo-EM) becomes popular for macromolecular
structure determination. However, the 2D images which Cryo-EM detects are of
high noise and often mixed with multiple heterogeneous conformations or
contamination, imposing a challenge for denoising. Traditional image denoising
methods can not remove Cryo-EM image noise well when the signal-noise-ratio
(SNR) of images is meager. Thus it is desired to develop new effective
denoising techniques to facilitate further research such as 3D reconstruction,
2D conformation classification, and so on. In this paper, we approach the
robust image denoising problem in Cryo-EM by a joint Autoencoder and Generative
Adversarial Networks (GAN) method. Equipped with robust $\ell_1$ Autoencoder
and some designs of robust $\beta$-GANs, one can stabilize the training of GANs
and achieve the state-of-the-art performance of robust denoising with low SNR
data and against possible information contamination. The method is evaluated by
both a heterogeneous conformational dataset on the Thermus aquaticus RNA
Polymerase (RNAP) and a homogenous dataset on the Plasmodium falciparum 80S
ribosome dataset (EMPIRE-10028), in terms of Mean Square Error (MSE), Peak
Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), as
well as heterogeneous conformation clustering. These results suggest that our
proposed methodology provides an effective tool for Cryo-EM 2D image denoising.
Our code is available in "https://github.com/ghl1995/denoise-gan-in-cryo-em".
