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Dataset results
82 results for “Denoising”
Denoised data for the manuscript "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data"
<p>This dataset contains denoised sections of Distributed Acoustic Sensing (DAS) data, generated using a J-invariant autoencoder. It facillitates the reproduction of the results presented in the paper "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data."</p> <p><br>Abstract:<br><br>One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing (DAS), we can observe seismicity over multiple kilometers. DAS systems turn common fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiberoptic cable that covered the entire glacier, from its accumulation to its ablation zone, recording seismicity for one month. The highly active and dynamic cryospheric environ ment, in combination with poor coupling, resulted in DAS data characterized by a low Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to ef fectively denoise this dataset.<br>We use a self-supervised J -invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g., stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.</p>
Convallaria dataset for microscopy image denoising benchmark as used in Probabilistic Noise2Void paper
<p>Convallaria dataset for microscopy image denoising benchmark as used in Probabilistic Noise2Void paper (https://ieeexplore.ieee.org/document/9098336)</p>
Subset of BioID dataset (https://www.bioid.com/facedb/) used for image denoising benchmark as used in DivNoising paper (https://arxiv.org/abs/2006.06072)
<p>The original BioID dataset comes from https://www.bioid.com/facedb/. </p> <p>A subset of original BioID dataset was used for image denoising benchmark (corrupted with zero mean Gaussian noise of std 15) as in DivNoising paper (https://arxiv.org/abs/2006.06072)</p>
Flywing (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
DSB (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>DSB n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Flywing (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Flywing n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Mouse (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
<p>Mouse n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)</p>
Benchmark denoising strategies on fMRIPrep output - input data
<p>All inputs required to build the figures and statistics in the jupyter book.<br> Including: summary statistics for the figures and average connectomes per dataset.<br> Untar the file and put it under the `inputs/` directory.</p> <p>Repository: https://github.com/SIMEXP/fmriprep-denoise-benchmark</p> <p>Book: https://simexp.github.io/fmriprep-denoise-benchmark/</p> <p> </p>
Evaluation of Cavernous Sinus Invasion by Pituitary Adenoma Using Deep Learning Based Denoising MR
ClinicalTrials.gov study NCT04268251. IPD Sharing: NO. Countries: 1. Publications: 1.
Diagnostic Yield of Deep Learning Based Denoising MRI in Cushing's Disease
ClinicalTrials.gov study NCT04121988. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Animal acoustic identification, denoising, and source separation using generative adversarial networks
Open the record for dataset details and reuse information.
Data from: Dual tree complex wavelet transform based signal denoising method exploiting neighbourhood dependencies and goodness of fit test
A novel signal denoising method is proposed whereby goodness of fit (GOF) test in combination with a majority classifications based neighbourhood filtering is employed on complex wavelet coefficients obtained by applying dual tree complex wavelet transform (DTCWT) on a noisy signal. The DT-CWT has proven to be a better tool for signal denoising as compared to the conventional discrete wavelet transform (DWT) owing to its approximate translation invariance. The proposed framework exploits statistical neighbourhood dependencies by performing the GOF test locally on the DT-CWT coefficients for their preliminary classification/detection as signal or noise. Next, a deterministic neighbourhood filtering approach based on majority noise classifications is employed to detect false classification of signal coefficients as noise (via the GOF test) which are subsequently restored. The proposed method shows competitive performance against the state of the art in signal denoising.
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Trained Simulated Patient Data
<p>DAPS Trained data</p>
Denoising Autoencoders for Phenotype Stratification (DAPS) Sample Trained Patient Data
<p>Data for: https://github.com/greenelab/DAPS</p>
DeepBacs – Escherichia coli MreB denoising dataset and CARE model
<p>Training and test images of MreB-sfGFP<sup>sw</sup> expressing <em>E. coli </em>cells for image denoising, as well as a trained CARE model.</p> <p>Additional information can be found on our <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example images show confocal images of labelled <em>E. coli</em> MreB filaments at low and high SNR.</p> <p> </p> <p><strong>Training and test dataset:</strong></p> <p> </p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence)</p> <p><strong>Microscopy data type</strong>: Confocal fluorescence images</p> <p><strong>Microscope</strong>: Leica SP8 confocal microscope with a 1.40 NA 63x oil immersion objective </p> <p><strong>Cell type</strong>: <em>E. coli</em> strain NO34 expressing MreB-sfGFPsw fusion protein (kindly provided by Zemer Gitai) </p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 512x512 (Pixel size: 45 nm)</p> <p> </p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 100 epochs (600 steps/epoch) on 1500 paired image patches (image dimensions: (512 x 512 px²), patch size: (64 x 64 px²), 50 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU and data was augmented by a factor of 4 using rotation and flipping.</p> <p>The model weights can be used with the ZeroCostDL4Mic CARE 2D notebook and the CSBDeep Fiji plugin.</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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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.