Self-supervised noise modeling and sparsity guided electron tomography volumetric image denoising

Cryo-Electron Tomography (cryo-ET) is a revolutionary technique for visualizing macromolecular structures in near-native states. However, the physical limitations of imaging instruments lead to cryo-ET volumetric images with very low Signal-to-Noise Ratio (SNR) with complex noise, which has a side effect on the downstream analysis of the characteristics of observed macromolecules. Additionally, existing methods for image denoising are difficult to be well generalized to the complex noise in cryo-ET volumes. In this work, we propose a self-supervised deep learning model for cryo-ET volumetric image denoising based on noise modeling and sparsity guidance (NMSG), achieved by learning the noise distribution in noisy cryo-ET volumes and introducing sparsity guidance to ensure smoothness. Firstly, a Generative Adversarial Network (GAN) is utilized to learn noise distribution in cryo-ET volumes and generate noisy volumes pair from single volume. Then, a new loss function is devised to both ensure the recovery of ultrastructure and local smoothness. Experiments are done on five real cryo-ET datasets and three simulated cryo-ET datasets. The comprehensive experimental results demonstrate that our method can perform reliable denoising by training on single noisy volume, achieving better results than state-of-the-art single volume-based methods and competitive with methods trained on large-scale datasets.

Automated summary

In this work, we propose a self-supervised deep learning model for cryo-ET volumetric image denoising based on noise modeling and sparsity guidance. Experimental results demonstrate that our method can achieve reliable denoising by training on single noisy volume and outperform existing methods.