Generative Adversarial Networks for Robust Cryo-EM Image Denoising

TL;DR

This paper introduces a novel joint Autoencoder and GAN-based method for denoising Cryo-EM images, significantly improving the quality of low SNR images and aiding structural analysis.

Contribution

It develops a robust denoising framework combining Autoencoder and GANs with specialized loss functions, achieving state-of-the-art results in Cryo-EM image denoising.

Findings

Outperforms traditional denoising methods on Cryo-EM datasets

Improves image quality metrics like MSE, PSNR, SSIM

Facilitates better conformational clustering in Cryo-EM analysis

Abstract

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".