A review of denoising methods in single-particle cryo-EM

Cryo-EM has become a vital technique for resolving macromolecular structures at near-atomic resolution, enabling the visualization of proteins and large molecular complexes. However, the images are often accompanied by extremely low SNR, which poses significant challenges for subsequent processes such as particle picking and 3D reconstruction. Effective denoising methods can substantially improve SNR, making downstream analyzes more accurate and reliable. Thus, image denoising is an essential step in cryo-EM data processing. This paper comprehensively reviews recent advances in image denoising methods for single-particle analysis, covering approaches from traditional filtering methods to the latest deep learning-based strategies. By analyzing and comparing mainstream denoising methods, this review aims to advance the field of single-particle cryo-EM denoising, facilitate the acquisition of higher-quality images, and offer valuable insights for researchers new to the field.