Dataset and Models for DeblurSRNet
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| Format: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866902152335589376 |
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| author | Podder, Soumyajit Tsai, Hsieh-Fu |
| author_facet | Podder, Soumyajit Tsai, Hsieh-Fu |
| contents | <p><strong>Overview:</strong></p> <p>The directory includes various subfolders and Python scripts associated with DeblurSRNet. Subfolders such as Actin, Brightfield NIH3T3, Cancer Tissue, Endoplasmic Reticulum Membrane, Microbeads, Microtubule, Mitochondria Outer Membrane, Nanobeads Confocal SIM, Nanobeads Widefield Confocal, and RFP HUVEC indicate different experimental categories or data types. Additionally, there are Python scripts such as DeblurSRNet Inference Single, DeblurSRNet Inference Multiple, DeblurSRNet Inference Video, DeblurSRNet Training Single, and DeblurSRNet Training Multiple, showing the presence of code for data training and inference related to image deblurring and super-resolution networks.</p> <p> </p> <p><strong>Directory Structure:</strong></p> <p>Actin</p> <p>Brightfield NIH3T3</p> <p>Cancer Tissue</p> <p>Endoplasmic Reticulum Membrane</p> <p>Microbeads</p> <p>Microtubule</p> <p>Mitochondria Outer Membrane</p> <p>Nanobeads Confocal SIM</p> <p>Nanobeads Widefield Confocal</p> <p>Results</p> <p>RFP HUVEC</p> <p>DeblurSRNet Inference Single Dataset.py</p> <p>DeblurSRNet Inference Multiple Dataset.py</p> <p>DeblurSRNet Inference Video Dataset.py</p> <p>DeblurSRNet Training Single Dataset.py</p> <p>DeblurSRNet Training Multiple Dataset.py</p> <p> </p> <p><strong>Content Description:</strong></p> <p>The subfolders contain imaging data related to biological structures and experimental conditions.</p> <p>The Python scripts under DeblurSRNet are used for inference and training of a super-resolution and deblurring network.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16731200 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Dataset and Models for DeblurSRNet Podder, Soumyajit Tsai, Hsieh-Fu <p><strong>Overview:</strong></p> <p>The directory includes various subfolders and Python scripts associated with DeblurSRNet. Subfolders such as Actin, Brightfield NIH3T3, Cancer Tissue, Endoplasmic Reticulum Membrane, Microbeads, Microtubule, Mitochondria Outer Membrane, Nanobeads Confocal SIM, Nanobeads Widefield Confocal, and RFP HUVEC indicate different experimental categories or data types. Additionally, there are Python scripts such as DeblurSRNet Inference Single, DeblurSRNet Inference Multiple, DeblurSRNet Inference Video, DeblurSRNet Training Single, and DeblurSRNet Training Multiple, showing the presence of code for data training and inference related to image deblurring and super-resolution networks.</p> <p> </p> <p><strong>Directory Structure:</strong></p> <p>Actin</p> <p>Brightfield NIH3T3</p> <p>Cancer Tissue</p> <p>Endoplasmic Reticulum Membrane</p> <p>Microbeads</p> <p>Microtubule</p> <p>Mitochondria Outer Membrane</p> <p>Nanobeads Confocal SIM</p> <p>Nanobeads Widefield Confocal</p> <p>Results</p> <p>RFP HUVEC</p> <p>DeblurSRNet Inference Single Dataset.py</p> <p>DeblurSRNet Inference Multiple Dataset.py</p> <p>DeblurSRNet Inference Video Dataset.py</p> <p>DeblurSRNet Training Single Dataset.py</p> <p>DeblurSRNet Training Multiple Dataset.py</p> <p> </p> <p><strong>Content Description:</strong></p> <p>The subfolders contain imaging data related to biological structures and experimental conditions.</p> <p>The Python scripts under DeblurSRNet are used for inference and training of a super-resolution and deblurring network.</p> |
| title | Dataset and Models for DeblurSRNet |
| url | https://doi.org/10.5281/zenodo.16731200 |