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21 results for “Image Quality Assessment”
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>
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>
OCTA image dataset with label annotation for quality assessment
<p>This dataset is publish by the research "<em>A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image</em>"</p> <p>Detail:</p> <p>OCTA image dataset with label annotation for quality assessment. sOCTA-3x3-10k: 10,480 3 × 3 mm<sup>2</sup> superficial vascular layer OCTA (sOCTA) images divided into three classes; sOCTA-6x6-14k: 14,042 6 × 6 mm<sup>2</sup> sOCTA images divided into three classes. </p> <p>GitHub: <a href="https://github.com/shanzha09/COIPS">https://github.com/shanzha09/COIPS</a></p> <p>These datasets are public available, if you use the dataset or our system in your research, please <strong>cite</strong> our paper: <em><code>A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image</code></em>.</p> <p>arXiv:<a href="https://arxiv.org/abs/2107.10476v1">https://arxiv.org/abs/2107.10476v1</a></p>
Performance Evaluation of Objective Quality Assessment Methods for Omnidirectional Images Under Emerging Compressions
<p>This dataset provides extensive data from large-scale subjective experiments, encompassing MOS values, eye-tracking data, and raw subjective scores, collected from two laboratories Brno University of Technology and Czech Technical University in Prague (BUT and CTU). This new dataset serves as a comprehensive foundation for future research endeavors into novel objective quality metrics for omnidirectional IQA (OIQA).</p> <p> </p> <p><strong>▷ </strong> contact: xsimka01@vut.cz</p>
CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms
<p>The CID2013 Camera Image Database consists of real images taken by consumer cameras and mobile phones. It is developed to provide useful tool to allow researchers target more commercially relevant distortions when developing processes of objective image quality assessment algorithms.</p> <p>The CID2013 database consists of 480 evaluated images captured by 79 imaging devices (mobile phones, DSC, DSLR) in six Image Sets. Note that the actual number of images in the database is 474. In Image Set II, Device 6 is evaluated twice as we wanted to test inter-observer reliablity. The scores are later combined into a single MOS value as the two evaluations correlated strongly.</p> <p>If you use this database in your research, we kindly ask that you follow the copyright notice bellow and cite the following paper:</p> <p>Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. and Häkkinen, J. “CID2013: a database for evaluating no-reference image quality assessment algorithms”, IEEE Transactions on Image Processing, vol. 24, no. 1, pp. 390-402, Jan. 2015. <a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6975172">[pdf]</a></p> <p><strong>Method</strong></p> <p>The images are evaluated by 188 observers using Dynamic Reference (DR-ACR) method (explained below). A separate scale realignment ACR data consisting evaluations from 34 observers is also included that allows to combine the data from the six image sets</p> <p>In other respects the DR-ACR method resembles very much a basic Absolute Category Rating (ACR) method (ITU-R 500-11), except the observers saw a slideshow of all the other images in the test depicting the same scene before every evaluation (See DR_demo.mp4). By seeing the other images in the test setup as reference the observers were more aware of the total variation of quality represented within a single image set. This improved their evaluation as they didn’t need to save the far ends of the scale in case there would be even more better or worse image later on the experiment. The DR-ACR method is explained in detail in:</p> <p>Mikko Nuutinen, Toni Virtanen, Tuomas Leisti, Terhi Mustonen, Jenni Radun, Jukka Häkkinen (2014) A new method for evaluating the subjective image quality of photographs : dynamic reference Multimedia Tools and Applications 75: 4. 2367-2391 Dec.</p> <p>Database contains consumer camera images and their subjective evaluations in mean opinion score (MOS), sharpness, graininess, lightness and color saturation scales. It includes the complete raw data and background information from the naïve observers used to evaluate the images. Subjects’ vision was controlled for the near visual acuity, near contrast vision (near F.A.C.T.) and color vision (Farnsworth D15) before the participation. They received movie tickets as a reward. Outlier removal is made for mean opinion score (MOS) evaluations using ITU-R 500-11 recommendations to ease out the implementation of the database.</p> <p><strong>Material</strong></p> <p>The images in CID2013 are intended to represent typical photographs that consumers might capture with their cameras. The photographed scenes were based partly on the Photospace approach described by I3A (CPIQ Initiative Phase 1 White Paper: Fundamentals and review of considered test methods, I3A, 2007) The I3A CPIQ project has migrated under IEEE.</p> <p><strong>The test environment</strong></p> <p>The room has been covered with medium gray curtains to diffuse the ambient illumination. Fluorescent lights (5800K) were positioned behind the monitors and reflected from the back wall covered with grey curtain to create dim and uniform ambient illumination in the room. The light hitting the monitors measured below 20 lx. The subject’s viewing distance (approximately 80 cm) was controlled by a line hanging from the ceiling, and they were instructed to keep their forehead steady next to the line. Because of the display size, images were scaled to a size of 1600 x 1200 pixels using the bicubic interpolation method. Eizo ColorEdge CG241W, with 1920x1200 pixel resolution, monitors in was calibrated to sRGB having target values of: 80 cd/m2, 6500K and gamma 2.2 using EyeOne Pro calibrator (X-rite co.).</p> <p> </p> <p>-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------</p> <p>Copyright (c) 2014 The University of Helsinki<br> All rights reserved.</p> <p>Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the videos, the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirely appear in all copies of this database, and the original source of this database,Visual Cognition research group (www.helsinki.fi/psychology/groups/visualcognition/index.htm) and the Institute of Behavioral Science (www.helsinki.fi/ibs/index.html) at the University Helsinki (www.helsinki.fi/university/), is acknowledged in any publication that reports research using this database. Individual videos and images may not be used outside the scope of this database (e.g. in marketing purposes) without prior permission.</p> <p>The database and our paper are to be cited in the bibliography as:</p> <p>-----------------------------------------------------------------------------<br> Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. and Häkkinen, J. “CID2013: a database for evaluating no-reference image quality assessment algorithms”, IEEE Transactions on Image Processing, 2014, In press.<br> -----------------------------------------------------------------------------</p> <p>LIMITATION OF LIABILITY</p> <p>UNIVERSITY OF HELSINKI SHALL IN NO CASE BE LIABLE IN CONTRACT, TORT OR OTHERWISE FOR ANY LOSS OF REVENUE, PROFIT, BUSINESS OR GOODWILL OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL OR PUNITIVE COST, DAMAGES OR EXPENSE OF ANY KIND HOWEVER CAUSED OR HOWEVER ARISING UNDER OR IN CONNECTION WITH THE USE OF THIS DATABASE.</p> <p>THE UNIVERSITY OF HELSINKI SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF HELSINKI HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.</p> <p>THIS AGREEMENT SHALL BE CONSTRUED AND INTERPRETED IN ACCORDANCE WITH THE LAWS OF FINLAND, EXCLUDING ITS RULES FOR CHOICE OF LAW.</p> <p>-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------</p> <p> </p>
Low-dose Computed Tomography Perceptual Image Quality Assessment Grand Challenge Dataset (MICCAI 2023)
<p>Image quality assessment (IQA) is extremely important in computed tomography (CT) imaging, since it facilitates the optimization of radiation dose and the development of novel algorithms in medical imaging, such as restoration. In addition, since an excessive dose of radiation can cause harmful effects in patients, generating high- quality images from low-dose images is a popular topic in the medical domain. However, even though peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are the most widely used evaluation metrics for these algorithms, their correlation with radiologists’ opinion of the image quality has been proven to be insufficient in previous studies, since they calculate the image score based on numeric pixel values (1-3). In addition, the need for pristine reference images to calculate these metrics makes them ineffective in real clinical environments, considering that pristine, high-quality images are often impossible to obtain due to the risk posed to patients as a result of radiation dosage. To overcome these limitations, several studies have aimed to develop a no-reference novel image quality metric that correlates well with radiologists’ opinion on image quality without any reference images (2, 4, 5).</p> <p>Nevertheless, due to the lack of open-source datasets specifically for CT IQA, experiments have been conducted with datasets that differ from each other, rendering their results incomparable and introducing difficulties in determining a standard image quality metric for CT imaging. Besides, unlike real low-dose CT images with quality degradation due to various combinations of artifacts, most studies are conducted with only one type of artifact (e.g., low-dose noise (6-11), view aliasing (12), metal artifacts (13), scattering (14-16), motion artifacts (17-22), etc.). Therefore, this challenge aims to 1) evaluate various NR-IQA models on CT images containing complex noise/artifacts, 2) to compare their correlations with scores produced by radiologists, and 3) to grant insights into the determination of the best-performing metric of CT imaging in terms of correlating with the perception of radiologists’.</p> <p>Furthermore, considering that low-dose CT images are achieved by reducing the number of projections per rotation and by reducing the X-ray current, the combination of two major artifacts, namely the sparse view streak and noise generated by these methods, is dealt with in this challenge so that the best-performing IQA model applicable in real clinical environments can be verified.</p> <p> </p> <p><strong>Funding Declaration:</strong></p> <p>This research was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022-00155966, Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University)), and by the National Research Foundation of Korea (NRF-2022R1A2C1092072), and by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174276, RS-2020-KD000016).</p> <p> </p> <p><strong>References:</strong></p> <ol> <li>Lee W, Cho E, Kim W, Choi J-H. Performance evaluation of image quality metrics for perceptual assessment of low-dose computed tomography images. Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment: SPIE, 2022.</li> <li>Lee W, Cho E, Kim W, Choi H, Beck KS, Yoon HJ, Baek J, Choi J-H. No-reference perceptual CT image quality assessment based on a self-supervised learning framework. Machine Learning: Science and Technology 2022.</li> <li>Choi D, Kim W, Lee J, Han M, Baek J, Choi J-H. Integration of 2D iteration and a 3D CNN-based model for multi-type artifact suppression in C-arm cone-beam CT. Machine Vision and Applications 2021;32(116):1-14.</li> <li>Pal D, Patel B, Wang A. SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI): IEEE, 2021; p. 1962-1965.</li> <li>Mittal A, Moorthy AK, Bovik AC. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 2012;21(12):4695-4708. doi: 10.1109/TIP.2012.2214050</li> <li>Lee J-YK, Wonjin; Lee, Yebin; Lee, Ji-Yeon; Ko, Eunji; Choi, Jang-Hwan. Unsupervised Domain Adaptation for Low-dose Computed Tomography Denoising. IEEE Access 2022.</li> <li>Jeon S-Y, Kim W, Choi J-H. MM-Net: Multi-frame and Multi-mask-based Unsupervised Deep Denoising for Low-dose Computed Tomography. IEEE Transactions on Radiation and Plasma Medical Sciences 2022.</li> <li>Kim W, Lee J, Kang M, Kim JS, Choi J-H. Wavelet subband-specific learning for low-dose computed tomography denoising. PloS one 2022;17(9):e0274308.</li> <li>Han M, Shim H, Baek J. Low-dose CT denoising via convolutional neural network with an observer loss function. Med Phys 2021;48(10):5727-5742. doi: 10.1002/mp.15161</li> <li>Kim B, Shim H, Baek J. Weakly-supervised progressive denoising with unpaired CT images. Med Image Anal 2021;71:102065. doi: 10.1016/j.media.2021.102065</li> <li>Wagner F, Thies M, Gu M, Huang Y, Pechmann S, Patwari M, Ploner S, Aust O, Uderhardt S, Schett G, Christiansen S, Maier A. Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography. Med Phys 2022;49(8):5107-5120. doi: 10.1002/mp.15718</li> <li>Kim B, Shim H, Baek J. A streak artifact reduction algorithm in sparse-view CT using a self-supervised neural representation. Med Phys 2022. doi: 10.1002/mp.15885</li> <li>Kim S, Ahn J, Kim B, Kim C, Baek J. Convolutional neural network-based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme. Med Phys 2022;49(9):6253-6277. doi: 10.1002/mp.15884</li> <li>Bier B, Berger M, Maier A, Kachelrieß M, Ritschl L, Müller K, Choi JH, Fahrig R. Scatter correction using a primary modulator on a clinical angiography Carm CT system. Med Phys 2017;44(9):e125-e137.</li> <li>Maul N, Roser P, Birkhold A, Kowarschik M, Zhong X, Strobel N, Maier A. Learning-based occupational x-ray scatter estimation. Phys Med Biol 2022;67(7). doi: 10.1088/1361-6560/ac58dc</li> <li>Roser P, Birkhold A, Preuhs A, Syben C, Felsner L, Hoppe E, Strobel N, Kowarschik M, Fahrig R, Maier A. X-Ray Scatter Estimation Using Deep Splines. IEEE Trans Med Imaging 2021;40(9):2272-2283. doi: 10.1109/TMI.2021.3074712</li> <li>Maier J, Nitschke M, Choi JH, Gold G, Fahrig R, Eskofier BM, Maier A. Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements. IEEE Trans Biomed Eng 2022;69(5):1608-1619. doi: 10.1109/TBME.2021.3123673</li> <li>Choi JH, Maier A, Keil A, Pal S, McWalter EJ, Beaupré GS, Gold GE, Fahrig R. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. II. Experiment. Med Phys 2014;41(6Part1):061902.</li> <li>Choi JH, Fahrig R, Keil A, Besier TF, Pal S, McWalter EJ, Beaupré GS, Maier A. Fiducial markerbased correction for involuntary motion in weightbearing Carm CT scanning of knees. Part I. Numerical modelbased optimization. Med Phys 2013;40(9):091905.</li> <li>Berger M, Muller K, Aichert A, Unberath M, Thies J, Choi JH, Fahrig R, Maier A. Marker-free motion correction in weight-bearing cone-beam CT of the knee joint. Med Phys 2016;43(3):1235-1248. doi: 10.1118/1.4941012</li> <li>Ko Y, Moon S, Baek J, Shim H. Rigid and non-rigid motion artifact reduction in X-ray CT using attention module. Med Image Anal 2021;67:101883. doi: 10.1016/j.media.2020.101883</li> <li>Preuhs A, Manhart M, Roser P, Hoppe E, Huang Y, Psychogios M, Kowarschik M, Maier A. Appearance Learning for Image-Based Motion Estimation in Tomography. IEEE Trans Med Imaging 2020;39(11):3667-3678. doi: 10.1109/TMI.2020.3002695</li> </ol>
Dataset - No Reference Image Quality assessment Scores for Humanities Online Repositories
<p>The dataset contains data on No-Reference Image Quality Assessment (NR-IQA) scores for online repositories in the humanities.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Assessing Environmental Factors in Healthcare Facilities in Order to Improve the Experience of Patients, Staff, and the Quality of Imaging Procedures
ClinicalTrials.gov study NCT03456895. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Improved Image Quality for Assessment of Carotid Artery Stenosis by Ultrafast Ultrasound FLOW Imaging
ClinicalTrials.gov study NCT06170580. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
99mTc-MIP-1404 for Imaging Prostate Cancer: Phase I Clinical Study to Assess the Image Quality of a Simplified Kit Formulation Compared to a Multi-step Preparation of 99mTc-MIP-1404
ClinicalTrials.gov study NCT01654874. IPD Sharing: Not stated. Countries: 1. Publications: 14.
Registry for Quality Assessment With Ultrasound Imaging and TTFM in Cardiac Bypass Surgery
ClinicalTrials.gov study NCT02385344. IPD Sharing: Not stated. Countries: 6. Publications: 3.
A Study of Flurpiridaz (18F) Injection for PET Imaging for Assessment of MPI Quality Using HPLC and SPE Manufacturing Processes
ClinicalTrials.gov study NCT04594941. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Xenetix® 350: Comparative Assessment of Image Quality for Coronary CT Angiography
ClinicalTrials.gov study NCT01255722. IPD Sharing: Not stated. Countries: 5. Publications: 0.
The Clinical Study Aims to Assess the Quality of Donor Livers Using Hyperspectral Imaging.
ClinicalTrials.gov study NCT06608667. IPD Sharing: NO. Countries: 1. Publications: 0.
Image Quality Assessment for Screening and Diagnostic Mammography
ClinicalTrials.gov study NCT02786004. IPD Sharing: NO. Countries: 1. Publications: 0.
Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis
ClinicalTrials.gov study NCT04289064. IPD Sharing: NO. Countries: 1. Publications: 0.
Modulation Contrast Microscopy Imaging for Oocyte Quality Assessment
ClinicalTrials.gov study NCT03683290. IPD Sharing: NO. Countries: 1. Publications: 0.
Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis
ClinicalTrials.gov study NCT04314180. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Image Quality in Obstetrical Ultrasonography: Comparison Between Subjective Assessment and Contrast-to-noise Ratio
ClinicalTrials.gov study NCT06265974. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.