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134 results for “Super Resolution”
live cell super-resolution data_dual_color_cell_line_Cohesin_depletion
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SEN2VENµS, a dataset for the training of Sentinel-2 super-resolution algorithms
<p><strong>1 Description</strong></p> <p><strong>SEN2VENµS</strong> is an open dataset for the super-resolution of Sentinel-2 images by leveraging simultaneous acquisitions with the VENµS satellite. The dataset is composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meters resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.</p> <p><strong>Changelog with respect to version 1.0.0</strong> (https://zenodo.org/records/6514159)</p> <ul> <li>All patches are now stored in indivual geoTiFF files with proper geo-referencing, regrouped in zip files per site and per category,</li> <li>The dataset now includes 20 meter resolution SWIR bands B11 and B12 from Sentinel-2 (L2A from Theia). Note that there is no HR reference for those bands, since the VENµS sensor has no SWIR band.</li> </ul> <p><strong>2 Files organization</strong></p> <p>The dataset is composed of separate sub-datasets embedded in separate zip files, one for each site, as described in table <a href="#org5e17b56">1</a>. Note that there might be slight variations in number of patches and number of pairs with respect to version 1.0.0, due do incorrect count of samples in previous version (an empty tensor was still accounted for).</p> <p>Table 1: Number of patches and pairs for each site, along with VENµS viewing zenith angle</p> <table> <tbody> <tr> <th>Site</th> <th>Number of patches</th> <th>Number of pairs</th> <th>VENµS Zenith Angle</th> </tr> </tbody> <tbody> <tr> <td>FR-LQ1</td> <td>4888</td> <td>18</td> <td>1.795402</td> </tr> <tr> <td>NARYN</td> <td>3813</td> <td>24</td> <td>5.010906</td> </tr> <tr> <td>FGMANAUS</td> <td>129</td> <td>4</td> <td>7.232127</td> </tr> <tr> <td>MAD-AMBO</td> <td>1442</td> <td>18</td> <td>14.788115</td> </tr> <tr> <td>ARM</td> <td>15859</td> <td>39</td> <td>15.160683</td> </tr> <tr> <td>BAMBENW2</td> <td>9018</td> <td>34</td> <td>17.766533</td> </tr> <tr> <td>ES-IC3XG</td> <td>8822</td> <td>34</td> <td>18.807686</td> </tr> <tr> <td>ANJI</td> <td>2312</td> <td>14</td> <td>19.310494</td> </tr> <tr> <td>ATTO</td> <td>2258</td> <td>9</td> <td>22.048651</td> </tr> <tr> <td>ESGISB-3</td> <td>6057</td> <td>19</td> <td>23.683871</td> </tr> <tr> <td>ESGISB-1</td> <td>2891</td> <td>12</td> <td>24.561609</td> </tr> <tr> <td>FR-BIL</td> <td>7105</td> <td>30</td> <td>24.802892</td> </tr> <tr> <td>K34-AMAZ</td> <td>1384</td> <td>20</td> <td>24.982675</td> </tr> <tr> <td>ESGISB-2</td> <td>3067</td> <td>13</td> <td>26.209776</td> </tr> <tr> <td>ALSACE</td> <td>2653</td> <td>16</td> <td>26.877071</td> </tr> <tr> <td>LERIDA-1</td> <td>2281</td> <td>5</td> <td>28.524780</td> </tr> <tr> <td>ESTUAMAR</td> <td>911</td> <td>12</td> <td>28.871947</td> </tr> <tr> <td>SUDOUE-5</td> <td>2176</td> <td>20</td> <td>29.170244</td> </tr> <tr> <td>KUDALIAR</td> <td>7269</td> <td>20</td> <td>29.180855</td> </tr> <tr> <td>SUDOUE-6</td> <td>2435</td> <td>14</td> <td>29.192055</td> </tr> <tr> <td>SUDOUE-4</td> <td>935</td> <td>7</td> <td>29.516127</td> </tr> <tr> <td>SUDOUE-3</td> <td>5363</td> <td>14</td> <td>29.998115</td> </tr> <tr> <td>SO1</td> <td>12018</td> <td>36</td> <td>30.255978</td> </tr> <tr> <td>SUDOUE-2</td> <td>9700</td> <td>27</td> <td>31.295256</td> </tr> <tr> <td>ES-LTERA</td> <td>1701</td> <td>19</td> <td>31.971764</td> </tr> <tr> <td>FR-LAM</td> <td>7299</td> <td>22</td> <td>32.054056</td> </tr> <tr> <td>SO2</td> <td>738</td> <td>22</td> <td>32.218481</td> </tr> <tr> <td>BENGA</td> <td>5857</td> <td>28</td> <td>32.587334</td> </tr> <tr> <td>JAM2018</td> <td>2564</td> <td>18</td> <td>33.718953</td> </tr> </tbody> </table> <p> </p> <p>Each site zip file contains a subfolder with the site name. This subfolder contains secondary zip files for each date, following this naming convention as the pair <code>id</code>: <code>{site_name}_{acquisition_date}_{mgrs_tile}</code>. For each date, 5 zip files are available, as shown in table <a href="#org504e2aa">2</a>.Each zip file contain subfolder <code>{bands}/{resolution}/</code> in which one GeoTiFF file per patch is stored, with the following naming convention: <code>{site_name}_{idx}_{acquisition_date}_{mgr_tile}_{bands}_{resolution}.tif</code>. Pixel values are encoded as 16 bits signed integers and should be converted back to floating point surface reflectance by dividing each and every value by 10 000 upon reading.</p> <p>Table 2: Naming convention for zip files associated to each date.</p> <table> <tbody> <tr> <th>File</th> <th>Content</th> </tr> </tbody> <tbody> <tr> <td><code>{id}_05m_b2b3b4b8.zip</code></td> <td>5m patches (\(256\times256\) pix.) for S2 B2, B3, B4 and B8 (from VENµS)</td> </tr> <tr> <td><code>{id}_10m_b2b3b4b8.zip</code></td> <td>10m patches (\(128\times128\) pix.) for S2 B2, B3, B4 and B8 (from Sentinel-2)</td> </tr> <tr> <td><code>{id}_05m_b5b6b7b8a.zip</code></td> <td>5m patches (\(256\times256\) pix.) for S2 B5, B6, B7 and B8A (from VENµS)</td> </tr> <tr> <td><code>{id}_20m_b5b6b7b8a.zip</code></td> <td>20m patches (\(64\times64\) pix.) for S2 B5, B6, B7 and B8A (from Sentinel-2)</td> </tr> <tr> <td><code>{id}_20m_b11b12.zip</code></td> <td>20m patches (\(64\times64\) pix.) for S2 B11 and B12 (from Sentinel-2)</td> </tr> </tbody> </table> <p> </p> <p>Each file comes with a master <code>index.csv</code> CSV (Comma Separated Values) file, with one row for each pair sampled in the given site. Columns are named after the <code>{bands}_{resolution}</code> pattern, and contains the full path to the corresponding GeoTiFF wihin the corresponding zip file:</p> <p><code>{site}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.zip/{bands}/{resolution}/{site}_{idx}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.tif</code></p> <p><strong>3 Licencing</strong></p> <p><strong>3.1 Sentinel-2 patches</strong></p> <p><strong>3.1.1 Copyright</strong></p> <p>Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres. Note: Copernicus Sentinel-2 Level 1C data is subject to this license: <a href="https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf">https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf</a></p> <p><strong>3.1.2 Licence</strong></p> <p>Files <code>*_b2b3b4b8_10m.tif</code>, <code>*_b5b6b7b8a_20m.tif</code> and <code>*_b11b12_20m.tif</code> are distributed under the the original licence of the Sentinel-2 Theia L2A products, which is the Etalab Open Licence Version 2.0 <sup><a href="#fn.2">2</a></sup>.</p> <p><strong>3.2 VENµS patches</strong></p> <p><strong>3.2.1 Copyright</strong></p> <p>Value-added data processed by CNES for the Theia data centre www.theia-land.fr using VENµS satellite imagery from CNES and Israeli Space Agency. The processing uses algorithms developed by Theia's Scientific Expertise Centres.</p> <p>3.2.2 <strong>Licence</strong></p> <p>Files <code>*_b2b3b4b8_05m.tif</code> and <code>*_b5b6b7b8a_05m.tif</code> are distributed under the original licence of the VENµS products, which is Creative Commons BY-NC 4.0 <sup><a href="#fn.3">3</a></sup>.</p> <p><strong>3.3 Remaining files</strong></p> <p>All remaining files are distributed under the Creative Commons BY 4.0 <sup><a href="#fn.4">4</a></sup> licence.</p> <p><strong>4 Note to users</strong></p> <p>Note that even if the VenµS2 dataset is sorted by sites and by pairs, we strongly encourage users to apply the full set of machine learning best practices when using it : random keeping separate pairs (or even sites) for testing purpose, and randomization of patches accross sites and pairs in the training and validation sets.</p> <p><strong>5 Citing</strong></p> <p>Please cite the following data paper (preprint, submitted to <em>MDPI Data</em>) and zenodo link when publishing work derived from this dataset:</p> <p>Michel, J.; Vinasco-Salinas, J.; Inglada, J.; Hagolle, O. SEN2VENµS, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms. <em>Data</em> <strong>2022</strong>, <em>7</em>, 96. https://doi.org/10.3390/data7070096</p> <p><a href="https://zenodo.org/deposit/6514159">10.5281/zenodo.14603764</a></p> <p><strong>Footnotes:</strong></p> <p><sup><a href="#fnr.1">1</a></sup></p> <p><a href="https://pytorch.org/">https://pytorch.org/</a></p> <p><sup><a href="#fnr.2">2</a></sup></p> <p><a href="https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf">https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf</a></p> <p><sup><a href="#fnr.3">3</a></sup></p> <p><a href="https://creativecommons.org/licenses/by-nc/4.0/">https://creativecommons.org/licenses/by-nc/4.0/</a></p> <p><sup><a href="#fnr.4">4</a></sup></p> <p><a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Ozone Super Resolution Video Supplement
<p>Video Supplement for: "Downscaling Atmospheric Chemistry Simulations with Physically Consistent Deep Learning," by Andrew Geiss, Sam J. Silva, and Joseph C. Hardin, (2022)</p>
Transthoracic contrast echocardiography data of a patient for super-resolution ultrasound localisation microscopy
<p>We provide sample contrast echocardiography datasets used for generation of super-resolution ultrasound localisation microscopy (ULM) images as reported in the following paper:</p> <div> <div>Jipeng Yan, Biao Huang, Johanna Tonko, Matthieu Toulemonde, Joseph Hansen-Shearer, Qingyuan Tan, Kai Riemer, Konstantinos Ntagiantas, Rasheda A Chowdhury, Pier D Lambiase, Roxy Senior, Meng-Xing Tang. Transthoracic Ultrasound Localization Microscopy of Myocardial Vasculature in Patients, Nature Biomedical Engineering, 2024. DOI: (<a href="https://www.nature.com/articles/s41551-024-01206-6" target="_blank" rel="noopener noreferrer">10.1038/s41551-024-01206-6</a>).</div> <div> </div> <div>The sample datasets include:</div> <div> </div> <div>"LogCompressedCEUS.mp4' is a video of CEUS images gated within one cardiac cycle after motion correction and log compression (acquistion time: 0.36s).</div> <div> </div> <div>"LinearScaleCEUS.mat" is a Matlab data file containing CEUS images after motion correction.</div> <div> </div> <div>"LinearScaleCEUSAfterNoiseReduction.mat' is a Matlab data file containing above CEUS images with noise reduced, which can be processed with our SRUS software (<a href="https://github.com/JipengYan1995/SRUSSoftware">JipengYan1995/SRUSSoftware</a>) for localisation and tracking (A brief tutorial can be found in "Usage of sample data in SRUS Software.docx").</div> <div> </div> <div>"RcvDataSample.mat' is a Matlab data file containing RF data in channels;</div> <div> </div> <div>"BFInformation.mat' is a Matlab data file containing parameters for beamforming;</div> <div> </div> <div>"Data Description.txt" contains more detailed descriptions of above data.</div> <div> </div> </div> <p><strong>Data from all the 10 cardiac cycles of this patient will be available in the future.</strong></p> <p>If you have any questions, please contact Meng-Xing Tang (email: mengxing.tang@imperial.ac.uk).</p>
Dataset supporting the submission to the journal "Ocean Dynamic" and titled "Hybrid covariance super-resolution data assimilation"
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Super-resolution in LWIR using 6 cameras
<p>Example of super-resolution in LWIR using 6 cameras, showing one of the original videos at the top, and the super-resolved video in the bottom, where improvement in resolution and SNR is observed.</p>
Rapid mapping of flood inundation by deep learning-based image super-resolution
<div> <div># Rapid mapping of flood inundation by deep learning-based image super-resolution</div> <div># Developer: Wenke Song</div> <div># The University of Hong Kong</div> <div># Contact email: songwk@connect.hku.hk</div> <div># MIT License</div> <div># Copyright (c) 2024 songwk0924</div> <div> </div> <div>There are two folders in the compressed file: Trained_model and Test_cases:</div> <div>(1) Trained_model</div> <div> model_d_DenseUnet.pth, for predicting the maximum water depth;</div> <div> model_v_DenseUnet.pth, for predicting the maximum velocity.</div> <div> </div> <div>(2) Test_cases</div> <div> Test_d_r1.npy, Test_d_r2.npy, Test_d_r3.npy: Input features for predicting maximum water depth of rainfall events r1-r3;</div> <div> Test_v_r1.npy, Test_v_r2.npy, Test_v_r3.npy: Input features for predicting maximum velocity of rainfall events r1-r3; <div> </div> </div> <div> bathy_mat_5m_0p.csv: Elevation data to create mask layer;</div> <div> Fine_grid_flood_maps (2DSWEs):</div> <div> hmax_r1.asc, hmax_r2.asc, hmax_r3.asc, maximum water depth simulated by 2DSWEs of rainfall events r1-r3;</div> <div> velmax_r1.asc, velmax_r2.asc, velmax_r3.asc, maximum velocity simulated by 2DSWEs of rainfall events r1-r3;</div> <div> </div> <div><span>The aforementioned data will be used as input for model prediction (Prediction.py). </span></div> <div><a name="OLE_LINK902"></a><a name="OLE_LINK909"></a><a href="https://github.com/songwk0924/Flood-inundation-mapping"><span>https://github.com/songwk0924/Flood-inundation-mapping</span></a></div> </div>
Super resolution validation results
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Transferability of single-image super resolution to geophysical downscaling
<p>Data to accompany paper "Transferability of single-image super resolution to geophysical downscaling" - Zhongyang Hu, Peter Kuipers Munneke, Stef Lhermitte, Yao Sun, Brice Noël, Melchior van Wessem, Lichao Mou, and Xiao Xiang Zhu. 2022</p> <p>RACMO2 27 km and 5.5 km simulations are freely available from (\url{https://www.projects.science.uu.nl/iceclimate/models/racmo-model.php#1-1}, IMAU, 2022; latest accessed on 3 October 2022) are provided by Van Wessem et al. (2018, 2016), for details and further usage, please refer to: </p> <p>Van Wessem, J.M., Ligtenberg, S.R.M., Reijmer, C.H., Van De Berg, W.J., Van Den Broeke, M.R., Barrand, N.E., Thomas, E.R., Turner, J., Wuite, J., Scambos, T.A. and Van Meijgaard, E., 2016. The modelled surface mass balance of the Antarctic Peninsula at 5.5 km horizontal resolution. <em>The Cryosphere</em>, <em>10</em>(1), pp.271-285.</p> <p>Van Wessem, J.M., Van De Berg, W.J., Noël, B.P., Van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C.L., Krüger, K., Lenaerts, J., Lhermitte, S. and Ligtenberg, S.R., 2018. Modelling the climate and surface mass balance of polar ice sheets using RACMO2–Part 2: Antarctica (1979–2016). <em>The Cryosphere</em>, <em>12</em>(4), pp.1479-1498.</p>
Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks
<p>Datasets and source codes for the manuscript "Surrogate Downscaling of Mesoscale Wind Fields Using Ensemble Super-Resolution Convolutional Neural Networks" submitted to the journal "Artificial Intelligence for the Earth Systems" of the American Meteorological Society.</p>
Supplementary dataset for 'Super-resolution vibrational imaging based on photoswitchable Raman probe'
<p>Here's a dataset for 'Super-resolution vibrational imaging based on photoswitchable Raman probe.' </p>
Resolution Enhancement and Deblurring of Porous Media μ-CT Images based on Super Resolution Generative Adversarial Network
<p>The above is the Data2 of Super Resolution Generative Adversarial Network based on High-Resolution Representation Learning.</p>
HEK293T cell super-resolution images by SoRa microscopy
<p>These images contain both untreated and VPA-treated cells, stained by H3K27ac antibodies, CCCTC binding factor (CTCF) antibodies and DNA fluorescent dye Hoechst. Cells were imaged by Yokogawa CSU-W1 SoRa super-resolution spinning disc confocal system (Tokyo, Japan).</p><p>There are two tar.zg files, <a href="https://zenodo.org/uploads/10032412">original_multi-cells_SoRa.tar.gz </a>and <a href="https://zenodo.org/uploads/10032412">single-cells_segmented.tar.gz</a>. "original_multi-cells_Sora.tar.gz" is the original image data, which have multiple cells in each image. On the other hand, "single-cells_segmented.tar.gz" is single-cell image data, obtained from "original_multi-cells_Sora.tar.gz" by segmentation processing.</p>
Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI
ClinicalTrials.gov study NCT05820113. IPD Sharing: NO. Countries: 1. Publications: 19.
3-D Super Resolution Ultrasound Microvascular Imaging
ClinicalTrials.gov study NCT04136912. IPD Sharing: YES. Countries: 1. Publications: 0.
SUper-Resolution Ultrasound Imaging of Erythrocytes (SURE) in Normal and Malignant Lymph Nodes
ClinicalTrials.gov study NCT05754814. IPD Sharing: UNDECIDED. Countries: 1. Publications: 36.
Clinical Application of Super-resolution Ultrasound(SR-US) Imaging in Solid Tumors
ClinicalTrials.gov study NCT06018142. IPD Sharing: NO. Countries: 1. Publications: 2.
Arid or Cloudy: Characterizing the Atmosphere of the super-Earth 55 Cancri e using High-Resolution Spectroscopy
<p>These raw data and models are supplementary to the submission of the manuscript.</p>
Micro-stepping Extended Focus reduces photobleaching and preserves structured illumination super-resolution features
<p><span>Despite progress made in confocal microscopy, even fast systems still have insufficient temporal resolution for detailed live cell volume imaging, such as tracking rapid movement of membrane vesicles in three-dimensional space. Depending on the shortfall, this may result in undersampling and/or motion artifacts that ultimately limit the quality of the imaging data. By sacrificing the detailed information in the Z-direction, we propose a new imaging modality that involves capturing fast "<i>projections</i>"<i> </i>from the field of depth which shortens imaging time by approximately an order of magnitude as compared to standard volumetric confocal imaging. With faster imaging, radiation exposure to the sample is reduced, resulting in less fluorophore photobleaching and potential photodamage. The implementation minimally requires two synchronized control signals that drive a piezo stage and trigger the camera exposure. The device generating the signals has been tested on spinning disk confocals and instant structured-illumination-microscopy (iSIM) microscopes. Our calibration images show that the approach provides highly repeatable and stable imaging conditions that enable photometric measurements of the acquired data, in both standard live imaging and super-resolution modes. </span></p>
4x and 10x Super Resolution Generator Models Trained With Planet CubeSat Satellite Imagery
<p>Final resampling generator models produced from the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) (https://github.com/xinntao/ESRGAN). ESRGAN was trained at two different resampling factors, 4x and 10x, using a training data set of global Planet CubeSat satellite images. These generators can be used to resample Planet CubeSat satellite images from 30m and 12m to 3m resolution. Descriptions and results of training can be found at https://wandb.ai/elezine/pixelsmasher. In press at Canadian Journal of Remote Sensing: Super-resolution surface water mapping on the Canadian Shield using Planet CubeSat images and a Generative Adversarial Network, Ekaterina M. D. Lezine, Ethan D. Kyzivat, and Laurence C. Smith (2021). </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.