Journal
ULTRAMICROSCOPY
Volume 255, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.ultramic.2023.113860
Keywords
Electron tomography; Image denoising; Self-supervised learning
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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.
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.
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