Therefore, an image restoration operation is usually necessary before particle picking, structure segmentation and other cryo-EM data analysis processes to attain high-resolution cryo-EM 3D reconstructions.Ī variety of conventional methods have been developed to improve the contrast and decrease the noise level in cryo-EM micrographs, such as BM3D ( Dabov et al., 2007), band-pass filter ( Penczek, 2010) and Wiener filter ( Sindelar and Grigorieff, 2011). However, the signal-to-noise ratio (SNR) of raw cryo-EM images is estimated to be only as high as 0.01–0.1 ( Bendory et al., 2020), among the lowest in any imaging field, which extremely decreases the accuracy and efficiency in downstream analysis of cryo-EM images and reduces the confidence of structures determination.
Topaz clean algorithm series#
Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles.Ĭryo-electron microscopy (cryo-EM) is a widely used technology that resolves high-resolution three-dimensional (3D) structures of protein and macromolecular complexes from a series of two-dimensional (2D) micrographs ( Bai et al., 2015). Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches.
Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise’s true properties.
Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser.
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