Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks

Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks

Blog Article

In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline.Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter.These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration.Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, ovs-02gt but it is not clear how these algorithms affect the information content of the image.Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed.

The study suggests that besides improving the visual contrast 2.75x100 of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment.These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking.