MLI-lab/DeepDeWedge
Self-supervised deep learning for denoising and missing wedge reconstruction of cryo-ET tomograms
This project helps cryo-electron tomography researchers improve the quality of their 3D images of biological samples. It takes noisy tomograms, which are 3D reconstructions with limited viewing angles (the 'missing wedge'), and processes them to produce clearer, more complete 3D structures, making it easier to visualize and analyze cellular components. Cryo-ET scientists, biologists, and structural biologists who work with electron microscopy data would use this.
No commits in the last 6 months.
Use this if you need to simultaneously reduce noise and reconstruct missing information in your cryogenic electron tomograms to get a clearer picture of your biological samples.
Not ideal if you are not working with cryo-electron tomography data or if your primary need is general image denoising outside of this specific domain.
Stars
49
Forks
13
Language
Jupyter Notebook
License
BSD-2-Clause
Category
Last pushed
Jul 29, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MLI-lab/DeepDeWedge"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
voxelmorph/voxelmorph
Unsupervised Learning for Image Registration
uncbiag/uniGradICON
uniGradICON: A Foundation Model for Medical Image Registration (MICCAI 2024)
junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration
TransMorph: Transformer for Unsupervised Medical Image Registration (PyTorch)
DeepRegNet/DeepReg
Medical image registration using deep learning
guopengf/FL-MRCM
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image...