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| Item Type: | Dataset |
|---|---|
| Title: | Data from: Denoising, deblurring, and optical deconvolution for cryo-ET and light microscopy with a physics-informed deep neural network DeBCR |
| Creators Name: | Li, Rui, Yushkevich, Artsemi, Chu, Xiaofeng, Kudryashev, Mikhail and Yakimovich, Arturo |
| Abstract: | Datasets deposition for a physics-informed deep learning model DeBCR for microscopy image restorations. Leveraging optics-based physical models as its foundation, DeBCR model demonstrates superior performance in denoising, optical deconvolution, and more general deblurring (super-resolution, SR) tasks across both light microscopy (LM) and cryo-electron microscopy (EM) modalities. To evaluate DeBCR on various image restoration tasks, several previously published datasets were assembled, pre-processed and provided as the following: LM: 2D denoising (files: LM_2D_CARE_*.npz) - low/high exposure confocal dataset of Schmidtea mediterranea (Denoising_Planaria) from the publication of CARE network applied to fluorescent microscopy data (Weigert, Schmidt, Boothe et al., Nature Methods, 2018). LM: 3D denoising (files: LM_3D_CARE_*.npz) - low/high exposure confocal dataset of Tribolium castaneum (Denoising_Triboleum) from the publication of CARE network applied to fluorescent microscopy data (Weigert, Schmidt, Boothe et al., Nature Methods, 2018). LM: wide-field data deconvolution (SR) (files: WF_SIM_S.aureus_*.npz) - widefield/SIM dataset of Staphylococcus aureus from the deposition (Pereira & Pinho, Zenodo, 2021). LM: confocal data deconvolution (SR) (files: CF_STED_FActin_*.npz) - confocal/STED dataset of F-actin from the deposition (Bouchard, Gagné & Lavoie-Cardinal, Zenodo, 2023). EM: low-frequency denoising (files: EM_low_Tomo110_*.npz) - cryoET dataset of Chlamydomonas reinhardtii cilia (Tomo110) from the cryo-CARE publication (Buchholz et al., IEEE (ISBI), 2019). EM: high-frequency denoising (files: EM_high_RyR1_*.npz) - cryoET dataset of native vesicles-embedded membrane protein RyR1 (EMPIAR-10452) from the publication (Sanchez, Zhang, Chen et al., Nat Commun, 2020). and each include train (*_train.npz), test (*_test.npz), and validation (*_val.npz) parts. |
| Keywords: | Image Processing, Deep Learning, Denoising, Deconvolution, Deblurring, Wavelets Theory, Cryo-Et, Light Microscopy |
| Source: | Zenodo |
| Publisher: | CERN |
| Date: | 4 July 2024 |
| Official Publication: | https://doi.org/10.5281/zenodo.12626122 |
| Related to: |
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