# Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Microscopy Data

arXiv: https://arxiv.org/abs/1810.05420 · HTML: https://mlsanity.com/paper/1810.05420/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [Interpretable Unsupervised Diversity Denoising and Artefact Removal](https://mlsanity.com/paper/2104.01374/) (2021)
- [Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement](https://mlsanity.com/paper/2105.08040/) (2021)
- [Deformation-Compensated Learning for Image Reconstruction without Ground Truth](https://mlsanity.com/paper/2107.05533/) (2021)
- [Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods](https://mlsanity.com/paper/2305.19079/) (2023)
- [N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture](https://mlsanity.com/paper/2211.08512/) (2022)
- [A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography](https://mlsanity.com/paper/2311.05539/) (2023)
- [Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated Noise](https://mlsanity.com/paper/2503.19468/) (2025)
- [DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM](https://mlsanity.com/paper/2410.11373/) (2024)
- [Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization](https://mlsanity.com/paper/2205.04464/) (2022)
- [Self-Supervised Single-Image Deconvolution with Siamese Neural Networks](https://mlsanity.com/paper/2308.09426/) (2023)
- [Sparse Bayesian Generative Modeling for Compressive Sensing](https://mlsanity.com/paper/2411.09483/) (2024)
- [Joint Denoising of Cryo-EM Projection Images using Polar Transformers](https://mlsanity.com/paper/2506.11283/) (2025)
- [Region of Interest Segmentation and Morphological Analysis for Membranes in Cryo-Electron Tomography](https://mlsanity.com/paper/2602.21195/) (2026)

## Abstract (arXiv)

Multiple approaches to use deep learning for image restoration have recently been proposed. Training such approaches requires well registered pairs of high and low quality images. While this is easily achievable for many imaging modalities, e.g. fluorescence light microscopy, for others it is not. Cryo-transmission electron microscopy (cryo-TEM) could profoundly benefit from improved denoising methods, unfortunately it is one of the latter. Here we show how recent advances in network training for image restoration tasks, i.e. denoising, can be applied to cryo-TEM data. We describe our proposed method and show how it can be applied to single cryo-TEM projections and whole cryo-tomographic image volumes. Our proposed restoration method dramatically increases contrast in cryo-TEM images, which improves the interpretability of the acquired data. Furthermore we show that automated downstream processing on restored image data, demonstrated on a dense segmentation task, leads to improved results.