Papers › Generative Adversarial Networks for Robust Cryo-EM Image Denoising
Generative Adversarial Networks for Robust Cryo-EM Image Denoising
Hanlin Gu, Yin Xian, Ilona Christy Unarta, Yuan YAO
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 ℓ₁ Autoencoder and some designs of robust β-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".
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections