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59 results for “Image sensing”
Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"
<p>These datasets accompany the article published in <em>Remote Sensing </em>entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing".</p> <p>For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included: </p> <ul> <li><strong>Raw Metrics: </strong>the raw evaluations for each image returned by each of the 12 evaluation metrics. </li> <li><strong>Rankings:</strong> the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). </li> </ul> <p>The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.</p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Pond Area Estimates: Nine Study Regions in Alaska for 3 time periods (1950s, 1978-1982, 1999-2001) using remotely sensed images
The data are ArcGIS shapefiles by USGS quadrangle within 9 study regions: Arctic Coastal Plain, Stevens Village area, Yukon Flats, Minto Flats, Denali Flats, Talkeetna, Innoko Flats, Tetlin Flats, and Copper River Basin. Each shapefile polygon represents the shoreline of a pond as visually interpreted from each georectified remotely sensed image. All images were rectified based on at least 25 control points from 1:63 360 USGS digital raster graphics topographic maps using a second-order polynomial with a RMS error of less than one satellite image pixel (30 meters). All closed-basin ponds greater than 0.2 hectares were visually delineated and manually traced as polygons using ArcGIS. Each pond polygon has an ID and Hectares field representing the pond ID and area in hectares for the time period of the remotely sensed image.
Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"
<p>Source files and reconstructions for "Simple 3D compressed sensing scheme for faster and less phototoxic fluorescence microscopy imaging"</p> <p>The source files are to be used with the code on https://github.com/MaximeMaW/CompressedSensingMicroscopy3D (also archived in https://zenodo.org/record/439690)</p> <ol> <li>The files prefixed with "VIZ" are high resolution TIF visualizations.</li> <li>The files come from three experiments on two different setups: <ol> <li>A lattice light sheet microscope (LLSM): beads sample (filed termed "<strong>lattice-beads</strong>" and actin-labelled mESCs (files termed "<strong>lattice-phalloidin</strong>")</li> <li>An epifluorescence microscope: beads sample (files termed "<strong>epifluorescence</strong>")</li> </ol> </li> <li>The acquisitions were either performed using an identity measurement matrix (mimicking the plane-by-plane acquisition mode of a traditional z-stack): files termes "<strong>reference</strong>" or with a Fourier measurement matrix (described in the code mentioned above) with a compression ratio of 2 (files termed "<strong>compressed</strong>".</li> <li>The reconstructions were performed as described in the paper with the code mentioned above. Several reconstructions were computed from the same compressed images by simulating increasing compression ratios. To do so, reconstructions were performed by selecting a subset of the acquired planes (number indicated as "<strong>**frames</strong>")</li> <li>Reconstructions were sparsified using a 2D PSF model computed for our epifliuorescence setup and the LLSM (files termed "<strong>PSF_model</strong>"). These are provided as numpy arrays.</li> </ol> <p> </p>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
OHS data provided by Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application
<p>Images from Chinese Orbita Hyperspectral Satellites (OHS) provided by <em>the Competition in Hyperspectral Remote Sensing Image Intelligent Processing Application</em> are shared. All the images have been radiometric calibrated and atmospheric corrected by the author.</p> <p>Paper: J. He, J. Li, Q. Yuan, H. Shen, and L. Zhang, "Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution," <em>IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)</em>, 2021.</p> <p>More information about the author can be found at https://jianghe96.github.io/</p> <p>If this dataset is helpful please cite as:</p> <pre>@article{he2021spectral, title={Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution}, author={He, Jiang and Li, Jie and Yuan, Qiangqiang and Shen, Huanfeng and Zhang, Liangpei}, journal={IEEE Transactions on Neural Networks and Learning Systems}, year={2021}, }</pre>
Sensing images of Chutou gully during 2019 to 2021
<p>We release three sets of sensing images, which were obtained on 7 February 2019, 26 February 2020, and 12 March 2021, in Chutou gully, , Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. These UAV images both can be viewed and edited in ENVI software.</p>
Image sensing with multilayer, nonlinear optical neural networks
<p>This data repository contains the information necessary to reproduce the main results of the paper “Image sensing with multiplayer, nonlinear optical neural networks”.</p> <p>This repository contains the data and the code for generating the figures in the manuscript "Image sensing with multilayer, nonlinear optical neural networks", including figures in the main text and in supplementary materials. The repository also contains the code for controling the experiment setup and running the experiments conducted in the paper:</p> <ul> <li>Folder 'Data_Collection_Example' and 'Data_Extraction_Example' contain example scripts for instrument control and data collection using the multilayer optical-neural-network sensor.</li> <li>Other folders are organized according to the figure panels in the main text, each containing the data and the code required to reproduce the plots in a main figure panel and its associated supplementary figures. In each of these folders, there is a README.txt file that summarizes the role of each file in the folder. </li> </ul>
Fig.1 Tomopteris pacifica. Developmental stages. SEM images. A in Development and structure of the anterior nervous system and sense organs in the holopelagic annelid Tomopteris spp. (Phyllodocida, Errantia)
Fig.1 Tomopteris pacifica. Developmental stages. SEM images. A Spherical trochophore ca. 48–72-h post-fertilization. B Elongated embryo at ca. 5 days post-fertilization (dpf) with four segments and rudiments of parapodia, Roman numerals refer to segment numbers. C Dorsal view of larva/juvenile at 6–7 dpf showing parapodia formation; note first cirruslike appendage. Nuchal organs (no) visible as cilia semicircles in front of the prototroch (pt). D Ventral view of larva/juvenile at ca. 8–10 dpf. E
Dataset used in "Ocean floor imaging with Distributed Acoustic Sensing and water phases reverberations" by Spica et al. in Geophysical Research Letters
<p>earthquake #1<br> earthquake #2</p>
Dataset - Impact of 3D radiative transfer on airborne NO2 imaging remote sensing over cities with buildings
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2021) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.md </strong></em>text file.</p> <p>The dataset contains:</p> <p>- libRadtran output (radiances and AMFs)</p> <p>- Synthetic SCDs</p>
An Agnostic Benchmark for Optical Remote Sensing Image Super-Resolution
<p>In remote sensing, image super-resolution (ISR) is a technique used to create high-resolution (HR) images from low-resolution (R) satellite images, giving a more detailed view of the Earth’s surface. However, with the constant development and introduction of new ISR algorithms, it can be challenging to stay updated on the latest advancements and evaluate their performance objectively. To address this issue, we introduce SRcheck, a Python package that provides an easy-to-use interface for comparing and benchmarking various ISR methods. SRcheck includes a range of datasets that consist of high-resolution and low-resolution image pairs, as well as a set of quantitative metrics for evaluating the performance of SISR algorithms.</p>
Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging - Data
<p>Raw data and simulation code for the paper "Evaluation of ultrasound sensors for transcranial photoacoustic sensing and imaging"</p>
Data and code for: Remote sensing of riverbank migration using particle image velocimetry
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Microscopic images and schematics illustrating processes of microenvironment sensing and cortical actomyosin partitioning in T cells
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Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'
<p><strong>Experimental Data for the Paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Hierarchical Fusion and Divergent Activation Based Weakly Supervised Learning for Object Detection from Remote Sensing Images' along with the experimental results, and the methods used for comparison.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The data used in our experiments that we have the copyright of [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a>.<br> The licences valid for the elements of this repository are discussed under point "3. Licenses" below.</p> <p><strong>1. Structure</strong></p> <p>The repository contains the following items:</p> <ol> <li>"CODE_AND_RESULTS.zip" with the source codes and results of our method and the comparison methods,</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>We now focus on the structure of the file CODE_AND_RESULTS.zip.<br> It contains the following items:</p> <ol> <li>The directory "new_methods" contains the source code and results of the new methods proposed in our paper.</li> <li>The directory "comparison" contains the source code of the two approaches used for comparison: ACoL [<a href="https://doi.org/10.1109/CVPR.2018.00144">A</a>] and DANet [<a href="http://doi.org/10.1109/ICCV.2019.00669">B</a>].</li> <li>The folder "tools_and_metrics" holds additional libraries, software tools, and metrics using in our experiments. </li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p>Inside the folder "new_methods," the following sub-folders are provided:</p> <ol> <li>"data" includes data loading code and code for how organizing the input data of the neural network.</li> <li>"expr" includes training code.</li> <li>"model" includes neural network model, basic network and additional modules, depending on the file name, including improved network, and comparison model.</li> <li>"utils" includes some used library functions and test codes when testing, including image segmentation, searching for the largest connected area and data visualization, etc. Verification on the WSADD dataset is done via test_airplane.py and on the DIOR dataset via val_model.py.</li> </ol> <p>In our experiments, we used two datasets:</p> <p>"WSADD" [<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>],[<a href="http://doi.org/10.5281/zenodo.3843229">B</a>], which is already published <a href="http://doi.org/10.5281/zenodo.3843229">on zenodo</a> under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.<br> The "<a href="https://doi.org/10.1109/CVPR.2018.00144">DIOR</a>" proposed in [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>].</p> <p><strong>2. References</strong></p> <p>[<a href="http://doi.org/10.1109/ACCESS.2020.3019956">A</a>] Z.-Z. Wu, T. Weise, Y. Wang, Y. Wang, Convolutional neural network based weakly supervised learning for aircraft detection from remote sensing image, <em>IEEE Access</em> 8 (2020) 158097-158106. doi:<a href="http://doi.org/10.1109/ACCESS.2020.3019956">10.1109/ACCESS.2020.3019956</a>. <br> [<a href="http://doi.org/10.5281/zenodo.3843229">B</a>] Z.-Z. Wu. Weakly Supervised Airplane Detection Dataset: WSADD. May 2020. zenodo.org. doi:<a href="http://doi.org/10.5281/zenodo.3843229">10.5281/zenodo.3843229</a>.<br> [<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">C</a>] K. Li, G. Wan, G. Cheng, L. Meng, J. Han, Object detection in optical remote sensing images: A survey and a new benchmark, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em> 159 (2020) 296-307. doi:<a href="http://doi.org/10.1016/j.isprsjprs.2019.11.023">10.1016/j.isprsjprs.2019.11.023</a>. <br> [<a href="https://doi.org/10.1109/CVPR.2018.00144">D</a>] X. Zhang, Y. Wei, J. Feng, Y. Yang, T. S. Huang, Adversarial complementary learning for weakly supervised object localization, in: <em>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</em> (CVPR'18), Jun. 18-22, 2018, Salt Lake City, UT, USA, IEEE Computer Society, 2018, pp. 1325-1334. doi:<a href="https://doi.org/10.1109/CVPR.2018.00144">10.1109/CVPR.2018.00144</a>. <br> [<a href="http://doi.org/10.1109/ICCV.2019.00669">E</a>] H. Xue, C. Liu, F. Wan, J. Jiao, X. Ji, Q. Ye, DANet: Divergent activation for weakly supervised object localization, in: <em>Proceedings of the IEEE/CVF International Conference on Computer Vision</em> (ICCV'19), Oct. 27-Nov. 2, 2019, Seoul, Korea, IEEE, 2019, pp. 6588-6597. doi:<a href="http://doi.org/10.1109/ICCV.2019.00669">10.1109/ICCV.2019.00669</a>.</p> <p><strong>3. Licenses</strong></p> <p>The following licenses apply for the files and folders in the archive "CODE_AND_RESULTS.zip":</p> <ul> <li>The files in the folder `new_methods` are under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder `comparison/ACoL` have been obtained from https://github.com/xiaomengyc/ACoL, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>We put our code and data under the </li> <li>The files in the folder "comparison/DANet" have been obtained from <a href="https://github.com/xuehaolan/DANet">https://github.com/xuehaolan/DANet</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/xuehaolan/">https://github.com/xuehaolan/</a>.</li> <li>The files in the folder "tools_and_metrics/detections_DIOR" are related to the repository <a href="https://github.com/rafaelpadilla/Object-Detection-Metrics">https://github.com/rafaelpadilla/Object-Detection-Metrics</a>, which is under the <a href="https://mit-license.org/">MIT License</a>, and therefore are under the same license.</li> <li>The files in the folder "tools_and_metrics/Nest-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/Nest">https://github.com/ZhouYanzhao/Nest</a>, which is under the <a href="https://mit-license.org/">MIT License</a>.</li> <li>The files in the folder "tools_and_metrics/PRM-pytorch" are based on the repository <a href="https://github.com/ZhouYanzhao/PRM">https://github.com/ZhouYanzhao/PRM</a>, which is an open source project without associated license at the time of this writing. They will therefore remain under the copyright of the user <a href="https://github.com/ZhouYanzhao/">https://github.com/ZhouYanzhao/</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><strong>4. Contact</strong></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, wuzz@hfuu.edu.cn<br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, tweise@hfuu.edu.cn, tweise@ustc.edu.cn</p> <p>Institute of Applied Optimization, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China<br> </p>
Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'
<p><strong>Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval' along with the experimental results.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The licences valid for the elements of this repository are discussed under point "2. Licenses" below.</p> <p><em><strong>1. Structure</strong></em></p> <p>The repository contains the following items:</p> <ol> <li>"data" - the results from our experiments</li> <li>"lib" - some external functions used in the experiments</li> <li>"make_data" - the training and test data</li> <li>"fmt-vgg.py" - the FMT-RAN model</li> <li>"stn.py" - the STN module of ST-RAN</li> <li>"st_ran.py" - the ST-RAN model</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p><strong><em>2. License</em></strong></p> <p>The following licenses apply for the files and folders:</p> <ul> <li>The files "stn.py" and "spatial_transformer_tutorial.py" in the folder "lib" are from the GitHub repository <a href="https://github.com/GHamrouni/stn-tuto">https://github.com/GHamrouni/stn-tuto</a> and therefore are under the copyright of its repository owner Ghassen Hamrouni.</li> <li>All other files are under the <a href="https://mit-license.org/">MIT License</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><em><strong>3. Contact</strong></em></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, <a href="mailto:wuzz@hfuu.edu.cn">wuzz@hfuu.edu.cn</a><br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a>, <a href="http://mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a></p> <p><a href="http://iao.hfuu.edu.cn">Institute of Applied Optimization</a>, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China</p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset 2
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into ten parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-04</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-05</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-06</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-07</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-08</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-09</li> </ol> <p>These tem parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>$ cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>$ tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 114 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" and "Atomic Force Microscopy Images of Various Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573 and http://dx.doi.org/10.5281/zenodo.60434. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Structure Assisted Compressed Sensing Reconstruction of Undersampled AFM Images Dataset
<p>This deposition contains the results from a simulation of reconstructions of undersampled atomic force microscopy (AFM) images. The reconstructions were obtained using weighted iterative thresholding compressed sensing algorithms.</p> <p>The deposition consists of:</p> <ol> <li>An HDF5 database containing the results from simulations of reconstructions of undersampled atomic force microscopy images (<em>weighted_it_reconstructions.hdf5</em>).</li> <li>The Python script which was used to create the database (<em>weighted_it_reconstructions.py</em>).</li> <li>MD5 and SHA256 checksums of the database and Python script files (<em>weighted_it_reconstructions.MD5SUMS / weighted_it_reconstructions.SHA256SUMS</em>).</li> </ol> <p>The HDF5 database is licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/) . Since the CC BY 4.0 license is not well suited for source code, the Python script is licensed under the BSD 2-Clause license (http://opensource.org/licenses/BSD-2-Clause) .</p> <p><strong>The files are provided as-is with no warranty as detailed in the above mentioned licenses.</strong></p> <p>The database is split into four parts:</p> <ol> <li>weighted_it_reconstructions.hdf5.tar.xz.part-00</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-01</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-02</li> <li>weighted_it_reconstructions.hdf5.tar.xz.part-03</li> </ol> <p>These four parts must be concatenated before the database can be extracted from the tar.xz archive. On Unix-like systems this may be done using:</p> <p><em>cat weighted_it_reconstructions.hdf5.tar.xz.part-* > weighted_it_reconstructions.hdf5.tar.xz</em></p> <p>after which the archive may be extracted, e.g., using:</p> <p><em>tar xfJ weighted_it_reconstructions.hdf5.tar.xz</em></p> <p><strong>WARNING: The extracted HDF5 database has a size of 70 GiB.</strong></p> <p>The simulation results in the database are based on "Atomic Force Microscopy Images of Cell Specimens" by Christian Rankl licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). The original images are available at http://dx.doi.org/10.5281/zenodo.17573. The original images are provided as-is without warranty of any kind. Both the original images as well as adapted images are part of the dataset. </p>
Data set: Al-Biruni Earth Radius Optimization with Deep Transfer Learning based Scene Image Classification on Remote Sensing Imagery
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