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82 results for “Denoising”
Demo Dataset for LF-denoising
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InSight denoised seismic event waveforms
<p>Denoised seismic event waveforms (20 sps): seismic data recorded by NASA InSight mission on Mars and processed as described with its advantages and limitations in: </p> <p>Dahmen, N., Clinton, J., Stähler, S., Meier, M.-A., Ceylan, S., Euchner, F., Kim, D. , Horleston, A, Durán, C.,Zenhäusern, G., Charalambous, C., Kawamura, T. and Giardini, D. (2024). <em>Revisiting Martian Seismicity with Deep Learning-Based Denoising. </em>Submitted to Geophysical Journal International.</p> <p>Velocity waveforms are in physical units, rotated to ZNE components with ID: XB.ELYDL.03.BHZ/N/E; based on original waveforms XB.ELYSE.02.BHZ/N/E</p> <p>MSEED fiiles for all event waveforms from the low frequency event family (based on revised version of Marsquake Service catalogue Version 14: https://doi.org/10.12686/a21) whenever 20 sps data are available.</p> <p>Read with ObsPy, e.g.: obspy.read('.../S0173a')</p>
Pretrained-Guided Conditional Diffusion Models for Microbiome Data Denoising
<p>All datasets and corresponding metadata used in mbVDiT.</p>
Statistically unbiased prediction enables accurate denoising of voltage imaging data
<p>Here we report SUPPORT (Statistically Unbiased Prediction utilizing sPatiOtempoRal information in imaging daTa), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatially neighboring pixels in the same time frame, even when its temporally adjacent frames do not provide useful information for statistical prediction. Such spatiotemporal dependency is captured and utilized to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulation and experiments, we show that SUPPORT enables precise denoising of voltage imaging data while preserving the underlying dynamics in the scene.</p> <p>We also show that SUPPORT can be used for denoising time-lapse fluorescence microscopy images of <em>Caenorhabditis elegans</em> (<em>C. elegans</em>), in which the imaging speed is not faster than the locomotion of the worm, as well as static volumetric images of <em>Penicillium</em> and mouse embryos. SUPPORT is exceptionally compelling for denoising voltage imaging and time-lapse imaging data, and is even effective for denoising calcium imaging data.</p> <p>For more details, please see the accompanying research publication "<a href="https://www.nature.com/articles/s41592-023-02005-8">Statistically unbiased prediction enables accurate denoising of voltage imaging data</a>".</p> <p> </p> <p>Datasets for publication titled "Statistically unbiased prediction enables accurate denoising of voltage imaging data"</p> <p><strong>Voltage imaging of paQuasAr6a: paQuasAr6a.zip</strong><br> paQuasAr6a/Q6a_Cell1<br> paQuasAr6a/Q6a_Cell2<br> paQuasAr6a/Q6a_Cell3<br> paQuasAr6a/Q6a_Cell4<br> paQuasAr6a/Q6a_Cell5<br> paQuasAr6a/Q6a_Cell6<br> paQuasAr6a/175118PP046_P8_pulse (10 ms)_q6<br> paQuasAr6a/181625PP046_P5_q6_pulse(50ms)<br> paQuasAr6a/183616PP046_P8_pulse (10 ms)_q6<br> paQuasAr6a/183717PP046_P5_q6_pulse(50ms)</p> <p><strong>Voltage imaging of Voltron2: Voltron2.zip</strong><br> Voltron2/Voltron_Cell1<br> Voltron2/Voltron_Cell2<br> Voltron2/Voltron_Cell3<br> Voltron2/Voltron_Cell3_2<br> Voltron2/Voltron_Cell4<br> Voltron2/Voltron_Cell5<br> Voltron2/Voltron_Cell6<br> Voltron2/Voltron_Cell7 (100f_s)<br> Voltron2/Voltron_Cell7_2 (100 f_s)</p> <p><strong>Voltage imaging of zArchon (zebrafish spinal cord): zebrafish_spinal_cord_N.zip</strong></p> <p><strong>Voltage imaging of SomArchon (mouse hippocampus neuron): SomArchon.zip</strong></p> <p><strong>Volumetric structural imaging of mouse embryo (Expansion microscopy): Expansion_microscopy.zip</strong><br> Expansion_microscopy/JEME208_2x_expanded_bone.tif<br> Expansion_microscopy/JEME208_2x_expanded_intenstine.tif<br> Expansion_microscopy/JEME209_2x_expanded_bone_1.tif<br> Expansion_microscopy/JEME209_2x_expanded_bone_2.tif<br> Expansion_microscopy/JEME209_2x_expanded_tail.tif</p> <p><strong>Volumetric structural imaging of <em>penicillium</em>: Penicillium.zip</strong><br> Penicillium/penicillium_low_snr.tif<br> --> Low SNR image<br> Penicillium/penicillium_high_snr.tif<br> --> High SNR image</p> <p><strong>Calcium imaging of zebrafish: Zebrafish.zip</strong><br> Zebrafish/zebrafish_multiple_brain_regions.tif<br> --> Multiple brain regions<br> Zebrafish/zebrafish_Cerebellar_plate.tif<br> --> Cerebellar plate<br> Zebrafish/zebrafish_Dorsal_telencephalon.tif<br> --> Dorsal telencephalon<br> Zebrafish/zebrafish_Medulla_oblongata.tif<br> --> Medulla oblongata<br> Zebrafish/zebrafish_Olfactory_bulb.tif<br> --> Olfactory bulb<br> Zebrafish/zebrafish_Optic_tectum.tif<br> --> Optic tectum<br> Zebrafish/zebrafish_Habenula.tif<br> --> Habenula</p>
Effectiveness of Ultra-low-dose Chest CT With AI Based Denoising Solution
ClinicalTrials.gov study NCT05398887. IPD Sharing: NO. Countries: 0. Publications: 14.
Data from: Dual tree complex wavelet transform based signal denoising method exploiting neighbourhood dependencies and goodness of fit test
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Denoised brains of the OASIS dataset post-classification
<p>These brains have been classified by IVAM and denoised using an Ising model.</p>
Stacked Dense Denoise-Segmentation CGLS synthetic reconstruction from 91 projection without ring artefacts
<p>The CGLS recontruction without ring artefacts from the 91 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p> <div> </div>
Stacked Dense Denoise-Segmentation Synthetic Annotations
<p>The volumetric annotations of <a href="https://doi.org/10.5281/zenodo.3986502">https://doi.org/10.5281/zenodo.3986502</a>, <a href="https://doi.org/10.5281/zenodo.3986506">https://doi.org/10.5281/zenodo.3986506</a>, <a href="https://doi.org/10.5281/zenodo.3986508">https://doi.org/10.5281/zenodo.3986508</a> and <a href="https://doi.org/10.5281/zenodo.3986504">https://doi.org/10.5281/zenodo.3986504</a> used in the Stacked Dense Denoise-Segmentation Network. For the annotation the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p> <div> </div>
Stacked Dense Denoise-Segmentation CGLS synthetic reconstruction from 91 projection with ring artefacts
<p>The CGLS recontruction with ring artefacts from the 91 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
Stacked Dense Denoise-Segmentation FBP synthetic reconstruction from 3601 projection with ring artefacts
<p>The FBP recontruction with ring artefacts from the 3601 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors
<p>Dataset for Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors<br> <br> Authors left anonymous for double-blind review</p>
Mouse actin dataset for microscopy image denoising benchmark as used in PPN2V paper
<p>Mouse actin dataset for microscopy image denoising benchmark as used in PPN2V paper (https://arxiv.org/abs/1911.12291)</p>
Mouse skull nuclei dataset for microscopy image denoising benchmark as used in PPN2V paper
<p>Mouse skull nuclei dataset for microscopy image denoising benchmark as used in PPN2V paper (https://arxiv.org/abs/1911.12291)</p>
Results of IMC-Denoise: a content aware pipeline to enhance Imaging Mass Cytometry
<ul> <li>Generated training sets: <ul> <li>training_set_supp_table6.zip</li> <li>training_set_supp_table7.zip</li> <li>training_set_supp_table8.zip</li> <li>training_set_supp_table9.zip</li> <li>training_set_supp_table10.zip</li> <li>training_set_supp_table11.zip</li> </ul> </li> <li>Trained weights of experimental data: <ul> <li>training_result_supp_table7.zip</li> <li>training_result_supp_table8.zip</li> <li>training_result_supp_table9.zip</li> <li>training_result_supp_table10.zip</li> <li>training_result_supp_table11.zip</li> </ul> </li> <li>Simulation results: <ul> <li>Simulation_results.zip</li> </ul> </li> <li>Experimental results: <ul> <li>Human_bone_marrow_IMC_denoising_results.zip</li> <li>Human_breast_cancer_IMC_denoising_results.zip</li> <li>Human_pancreatic_cancer_IMC_denoising_results.zip</li> <li>MIBI_denoising_results.zip</li> </ul> </li> <li>Ilastik-processed or manual-labeled results: <ul> <li>DIMR_Ilastik_results.zip</li> <li>background_removal_results.zip</li> <li>manual_annotated_public_datasets.zip</li> </ul> </li> <li>Extracted single cell data and the corresponding phenotyping results from both DIMR and DeepSNiF-based segmented cell masks: <ul> <li>Single_cell_analysis.zip</li> </ul> </li> </ul>
Ultra-Low Dose CT Denoising for Lung Nodule Detection
ClinicalTrials.gov study NCT03159377. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Development of an Algorithm to Denoise HFNO-generated Tracheal Sound
ClinicalTrials.gov study NCT06218017. IPD Sharing: YES. Countries: 1. Publications: 0.
Denoised brains of the OASIS dataset post-classification
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The data availability information of "Denoising CSAMT signals in the time domain based on long short-term memory"
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Data from: PyroClean: Denoising pyrosequences from protein-coding amplicons for the recovery of interspecific and intraspecific genetic variation
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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.