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164 results for “image quality”
Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Brachypodium distachyon images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p>Euphorbia peplus images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Arabidopsis thaliana images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Arabidopsis thaliana</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Oryza sativa images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Oryza sativa</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Solanum lycopersicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Solanum lycopersicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Ocimum basilicum images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE et al.
<p><em>Ocimum basilicum</em> images used in the paper entitled "Led Color Gradient As A New Screening Tool For Rapid Phenotyping Of Plant Responses To Light Quality" by Pierre LEJEUNE, Anthony FRATAMICO, Frédéric BOUCHÉ, Samuel HUERGA-FERNÁNDEZ, Pierre TOCQUIN, Claire PÉRILLEUX</p>
Subjective Quality Assessment of Foveated Omnidirectional Images in Virtual Reality (FOIQA)
<p>This study presents a novel dataset called 'Foveated Omnidirectional Image Quality Assessment' (FOIQA) for the subjective quality evaluation of foveated 2D omnidirectional images. This dataset addresses the limitations of existing datasets by leveraging a high-resolution head-mounted display and a gaze-contingent evaluation approach. We provide individual opinion scores, mean opinion scores, and gaze data associated with both the test and reference images. </p>
Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets
<h2>Abstract</h2> <p>The remarkable progress of deep learning in dermatological tasks has brought us closer to achieving diagnostic accuracies comparable to those of human experts. However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. Several factors can impact data quality, such as the presence of duplicates, data leakage across train-test partitions, mislabeled images, and the absence of a well-defined test partition. In this paper, we conduct meticulous analyses of three popular dermatological image datasets: DermaMNIST, its source HAM10000, and Fitzpatrick17k, uncovering these data quality issues, measure the effects of these problems on the benchmark results, and propose corrections to the datasets. Besides ensuring the reproducibility of our analysis, by making our analysis pipeline and the accompanying code publicly available, we aim to encourage similar explorations and to facilitate the identification and addressing of potential data quality issues in other large datasets.</p> <h2>Citation</h2> <p>If you find this project useful or if you use our newly proposed datasets and/or our analyses, please cite our paper.</p> <blockquote> <pre>Kumar Abhishek, Aditi Jain, Ghassan Hamarneh. "Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets". arXiv preprint arXiv:2401.14497, 2024. DOI: 10.48550/ARXIV.2401.14497.</pre> </blockquote> <p>The corresponding BibTeX entry is:</p> <blockquote> <p><code>@article{abhishek2024investigating,</code><br><code> title={Investigating the Quality of {DermaMNIST} and {Fitzpatrick17k} Dermatological Image Datasets},</code><br><code> author={Abhishek, Kumar and Jain, Aditi and Hamarneh, Ghassan},</code><br><code> journal={arXiv preprint arXiv:2401.14497},</code><br><code> doi = {10.48550/ARXIV.2401.14497},</code><br><code> url = {https://arxiv.org/abs/2401.14497},</code><br><code> year={2024}</code><br><code>}</code></p> </blockquote> <h2>Project Website</h2> <p>The results of the analysis, including the visualizations, are available on the project website: <a href="https://derm.cs.sfu.ca/critique/" target="_blank" rel="noopener">https://derm.cs.sfu.ca/critique/</a>.</p> <h2>Code</h2> <p>The accompanying code for this project is hosted on GitHub at <a title="Corrected-Skin-Image-Datasets" href="https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets" target="_blank" rel="noopener">https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets</a>.</p> <h2>License</h2> <p>The metadata files (<code>DermaMNIST-C.csv</code>, <code>DermaMNIST-E.csv</code>, <code>Fitzpatrick17k_DiagnosisMapping.xlsx</code>,<code>Fitzpatrick17k-C.csv</code>) contained in this repository are licensed under <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">the Creative Commons Attribution 4.0 International (<strong>CC BY 4.0</strong>) License</a>.</p> <p>The NPZ files associated with DermaMNIST-C (<code>dermamnist_corrected_28.npz</code>, <code>dermamnist_corrected_224.npz</code>) and DermaMNIST-E (<code>dermamnist_extended_28.npz</code>, <code>dermamnist_extended_224.npz</code>) contained in this repository are licensed under <a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank" rel="noopener">the Creative Commons Attribution-NonCommercial 4.0 International (<strong>CC BY-NC 4.0</strong>) License</a>.</p> <p>The code hosted on <a href="https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets" target="_blank" rel="noopener">GitHub</a> is licensed under <a href="https://github.com/kakumarabhishek/Corrected-Skin-Image-Datasets/blob/main/LICENSE" target="_blank" rel="noopener">the Apache License 2.0</a>.</p>
A subjective image quality assessment dataset of color graded inverse tone-mapped HDR images
<p>A subjective image quality assessment dataset that includes quality scores of HDR images generated by nine inverse tone mapping methods. The images in the dataset show a wide variety of artifacts commonly present in dynamic range expanded HDR images. Twelve image pairs comprised of an LDR image and its corresponding HDR version were used to conduct the subjective assessment study. These images contain scenes with a wide range of light conditions, representing challenging situations for dynamic range expansion methods.</p> <p>The image quality dataset includes subjective quality scores for 108 inverse HDR images obtained by the different dynamic range expansion methods, scaled in Just Objectionable Differences (JODs). In addition, it includes the raw data from pairwise comparisons obtained from subjective experimentation. The raw data is composed of 6480 trials collected from 15 human observers.</p> <p><strong>Files included</strong></p> <ul> <li>List of images used in our experiments (images.csv).</li> <li>LDR images used as input (ldr.zip).</li> <li>HDR images used as reference (hdr.zip).</li> <li>Inverse tone-mapped HDR images evaluated in our study (hdr_itmo.zip).</li> <li>Pairwise comparison results and JOD scores (subjective-scores.zip)</li> <li>The objective quality scores of the inverse tone-mapped HDR images, computed by each quality metric assessed (objective-scores.zip).</li> </ul> <p> </p>
UBC2019 - A dataset of subjective image quality of head-mounted displays
<p>This is the dataset described in the following publication:</p> <p>"A subjective method for evaluating foveated image quality in HMDs", V. Thirumalai, J. Ribera, J. Xiang, J. Zhang, M. Azimi, J. Kamali, P. Nasiopoulos, <em>Society of Information Display, Display Week</em> - May 2020, San Francisco, CA</p>
PhotIQA: A photoacoustic image data set with image quality ratings
<div>To support the development and testing of full- and no-reference IQA measures we assembled PhotIQA, a data set consisting of 1134 photoacoustic (PA) images reconstructed with 3 different algorithms and ratings by 2 experts across five quality properties (overall quality, edge visibility, homogeneity, inclusion and background intensity). To allow full-reference assessment, highly characterised imaging test objects were used, providing a ground truth. A detailed description of the data set can be found in [1], where also baseline experiments give first insights. </div> <div> </div> <div>We have decided to make the data set available to the research community under the Creative Commons Attribution 4.0 International license. If you use the data in your research, we kindly ask that you reference the repository and the corresponding paper</div> <div> </div> <div> <div>[1] A. Breger, J.Gröhl, C. Karner, T.R. Else, I. Selby, J. Weir-McCall, C.-B. Schönlieb: PhotIQA: A photoacoustic image data set with image quality ratings, preprint on arXiv: http://arxiv.org/abs/2507.03478, July 2025</div> <div> </div> <div>Update v2.0 (July 7): quality ratings of more properties are available now, including edge visibility, object homogeneity, intensity (background), intensity (inclusions), overall quality</div> <div> </div> <div>Update v2.1 (August 20): the z-scores of the raters have been added and updated for mos</div> </div>
Dataset related to article "Phantom‑based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM"
<p>The dataset comprises information from several DICOM tags extracted from digital mammography (DM), digital breast tomosynthesis (DBT), and contrast-enhanced mammography (CEM) images acquired in a phantom study aimed at characterizing the automatic exposure control (AEC) behavior of diverse mammography equipment. The final ten columns of the datasets encompass signal (mena pixel values, MPV) and noise (standard deviation, SD) measurements derived from phantom images. These measurements are used to compute several image quality metrics, including contrast, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), CNR relative difference in comparison to the 45 mm reference thickness, and a figure of merit (FOM) obtained by diving the squared CNR by the mean glandular dose (MGD).</p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
<p>In the article "Quality Control of Image Sensors using Gaseous Tritium Light Sources" (<a href="https://doi.org/10.1098/rsta.2021.0130)">https://doi.org/10.1098/rsta.2021.0130)</a> we propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (<em>betalights</em>). This dataset includes all the recorded data along with the scripts necessary to reproduce the results and figures.</p>
Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research
<p>Dataset related to the publication: "Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research"</p>
Database that contains all images (plus 180 more) employed in the article: "Image features for quality analysis of thick blood smears employed in malaria diagnosis"
<p>We share with you a bank of images obtained from microscopic fields of thick blood smears employed in the malaria diagnosis, and also the .csv file that contains the labels for each image.</p> <p>The images are saved with a unique name that is found in the first column of the .csv file. The second column contains the labels from each image, according to their unique names.</p> <p>The labeling process was done with the online toolbox Labelbox. Labelbox, "Labelbox," Online, 2020. [Online]. Available: https://labelbox.com </p> <p>If you are interested in using our database, cite our article as a way to recognize our work. We will be grateful for that. </p> <p>CITATION: Fong Amaris, W.M., Martinez, C., Cortés-Cortés, L.J. et al. Image features for quality analysis of thick blood smears employed in malaria diagnosis. Malar J 21, 74 (2022). https://doi.org/10.1186/s12936-022-04064-2</p> <p>URL of our paper: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-022-04064-2</p> <p><strong>--- This is the link where you can find our images Bank: https://drive.google.com/drive/folders/1Qrv0e4bSEtkeqtPABz-klQp-6D6OjU-X?usp=sharing </strong></p> <p>It is important you to know that along with this .txt file, we are sharing the .csv file that contains 600 names of images (in the first column) with their respective labels (second column aside)</p> <p>This file corresponds to the instructions of an extended label file related to 600 images (and 600 new labels) in contrast to our previous label file with 420 labels from 420 images (https://www.researchgate.net/publication/359439520_Database420LabelsInstructionstxt ; https://www.researchgate.net/publication/359438904_Database420Labelscsv).</p> <p>Best Regards</p> <p> </p>
Digital Pediatric Image Quality Phantoms and Simulations
<p>Pediatric IQ Phantoms is a dataset of virtual phantoms and their computed tomography (CT) images for assessing the image quality of image-based CT denoising products in pediatric patients. The phantoms in the dataset enable assessment of <a title="IEC Standard 61223-5-3" href="https://webstore.iec.ch/publication/59789" target="_blank" rel="noopener">standard measures of image quality</a> – CT number accuracy, noise magnitude, uniformity, contrast dependent spatial resolution, and low contrast detectability. These phantoms are designed to span the range of pediatric effective waist diameters (Table 1) and thus can be used to assess pediatric image quality performance.</p> Table 1: Pediatric subgroups investigated defined by <table><tbody> <tr> <td><strong>Subgroup</strong></td> <td><strong>Age Range</strong></td> <td><strong>Waist Diameter Range</strong></td> </tr> <tr> <td>Newborn</td> <td>≤ 1 mo</td> <td>≤ 11.5 cm</td> </tr> <tr> <td>Infant</td> <td>> 1 mo & < 2 yrs</td> <td>> 11.5 cm & ≤ 16.8 cm</td> </tr> <tr> <td>Child</td> <td>> 2 yrs & ≤ 12 yrs</td> <td>> 16.8 cm & ≤ 23.2 cm</td> </tr> <tr> <td>Adolescent</td> <td>> 12 yrs & < 21 yrs</td> <td>> 23.2 cm & < 34 cm</td> </tr> <tr> <td>Adult</td> <td>≥ 22 yrs</td> <td>≥ 34 cm</td> </tr> </tbody> </table> <p>These phantoms include:</p> <ul> <li>CTP404 multi-contrast phantom: for assessing CT number accuracy and contrast-dependent spatial resolution. <ul> <li>CTP404 is a modified version of the sensitometry module CTP404 from the Catphan 600 phantom (The Phantom Laboratory, Salem, NY).</li> <li>This cylindrical phantom has eight unique contrast inserts ranging from -1000 to +900 HU in a uniform background of 0 HU. In its standard size, CTP404 has a diameter of 150 mm with 12 mm diameter inserts. Due to the sharp intersection between the phantom background and multi-contrast inserts, this module was used to evaluate contrast-dependent image sharpness using the contrast-dependent modulation transfer function.2</li> </ul> </li> <li>MITA LCD phantom: for assessing low contrast detectability. <ul> <li>The MITA-LCD phantom is a cylindrical phantom filled with water-equivalent attenuation material and contains four low-contrast disk inserts of different size and contrast combinations: 3mm-14HU, 5mm-7HU, 7mm-5HU, 10mm-3HU.3,4</li> </ul> </li> <li>Uniform water phantom: for assessing noise and noise texture. <ul> <li>The uniform water phantom is a cylindrical phantom filled with water-equivalent attenuation material.</li> </ul> </li> </ul>
NEMA image quality phantom acquisition on the Siemens mMR scanner
<p>NEMA image quality (IQ) phantom data acquired on the Siemens Biograph mMR PET/MR scanner. 60 minutes of PET data were acquired. The list mode acquisition and associated files required for reconstruction are provided.</p>
The CAPA Apple Quality Grading Multi-Spectral Image Database
<p>The CAPA Apple Quality Grading Multi-Spectral Image Database consists of multispectral (450nm, 500nm, 750nm, and 800nm) images of health and defected apples of bi-color, manual segmentations of defected regions, and expert evaluations of the apples into 4 quality categories. The defect types consist of bruise, rot, flesh damage, frost damage, russet, etc. The database can be used for academic or research purposes with the aim of computer vision based apple quality inspection.</p> <p>The CAPA Apple Quality Grading Multi-Spectral Image Database is a propriety of ULG (Gembloux Agro-Bio Tech) - Belgium, and cannot be used without the consent of the ULG (Gembloux Agro-Bio Tech), Belgium. <br> For consent, contact<br> Devrim Unay, İzmir University of Economics, Turkey: unaydevrim@gmail.com<br> OR<br> Marie-France Destain, Gembloux Agro-Bio Tech, Belgium: mfdestain@ulg.ac.be</p> <p><br> In disseminating results using this database, <br> 1. the author should indicate in the manuscript that it was acquired by ULG (Gembloux Agro-Bio Tech), Belgium.<br> 2. cite the following article Kleynen, O., Leemans, V., & Destain, M.-F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49.</p> <p>Relevant publications:<br> Kleynen et al., 2003 O. Kleynen, V. Leemans and M.F. Destain, Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting. Postharv. Biol. Technol., 30 (2003), pp. 221–232.<br> Leemans and Destain, 2004 V. Leemans and M.F. Destain, A real-time grading method of apples based on features extracted from defects. J. Food Eng., 61 (2004), pp. 83–89.<br> Leemans et al., 2002 V. Leemans, H. Magein and M.F. Destain, On-line fruit grading according to their external quality using machine vision. Biosyst. Eng., 83 (2002), pp. 397–404.<br> Unay and Gosselin, 2006 D. Unay and B. Gosselin, Automatic defect detection of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharv. Biol. Technol., 42 (2006), pp. 271–279.<br> Unay and Gosselin, 2007 D. Unay and B. Gosselin, Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. J. Food Eng., 78 (2007), pp. 597–605.<br> Unay et al., 2011 Unay, D., Gosselin, B., Kleynen, O, Leemans, V., Destain, M.-F., Debeir, O, “Automatic Grading of Bi-Colored Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, 75(1), 204-212, 2011.<br> </p>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Dataset used in "Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope" (https://arxiv.org/abs/2107.00048)
<p>'x_train.p', 'y_train.p': pickle files for training split containing 50,757 samples</p> <p>'x_val.p', 'y_val.p': pickle file for validation split containing 5,640 samples</p> <p>'x_test.p', 'y_test.p': pickle file for test split containing 6,267 samples</p> <p>'feature_names.p': pickle file containing names of all 119 features</p>
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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)
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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.