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| Item Type: | Dataset |
|---|---|
| Title: | DeBCR: trained model weights for fluorescence microscopy image enhancement |
| Creators Name: | Li, Rui, Yushkevich, Artsemi, Chu, Xiaofeng, Kudryashev, Mikhail and Yakimovich, Artur |
| Abstract: | Deep learning trained model weights deposition for the DeBCR framework for microscopy images restoration. DeBCR is a deep learning framework for microscopy images restoration such as denoising and deconvolution (resolution enhancement). DeBCR implements the m-rBCR model - a multi-scale image restoration model, proposed by us earlier (Li, Kudryashev, Yakimovich, ECCV 2024, 2025). The previous version (v1) of this deposition contains the re-processed previously published datasets provided as training/test/validation data. In the current version (v2) we provide the respective weights of our deep learning model, trained on these data: Confocal data denoising (trained DeBCR model: LM_CARE_S.mediterranea.zip) - 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). Confocal data denoising (trained DeBCR model: LM_CARE_T.castaneum.zip) - 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). Widefield data resolution enhancement (trained DeBCR model: WF_SIM_S.aureus.zip) - widefield/SIM dataset of Staphylococcus aureus from the deposition (Pereira & Pinho, Zenodo, 2021). Confocal data resolution enhancement (trained DeBCR model: CF_STED_FActin.zip) - confocal/STED dataset of F-actin from the deposition (Bouchard, Gagné & Lavoie-Cardinal, Zenodo, 2023). The core code and Python API interface: debcr (PyPI), 10.5281/zenodo.17673859 (Zenodo). The Napari GUI interface: napari-debcr (PyPI), 10.5281/zenodo.17674104 (Zenodo). |
| Keywords: | Image Processing, Image Restoration, Image Enhancement, Deep Learning, BCR, BCR-Net, m-rBCR, Denoising, Deblurring, Deconvolution, Confocal Microscopy, Widefield Microscopy, Fluorescence Microscopy, Light Microscopy |
| Source: | Zenodo |
| Publisher: | CERN |
| Date: | 21 November 2025 |
| Official Publication: | https://doi.org/10.5281/zenodo.15575890 |
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