The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided. * Total number of non-empty WordNet synsets: 21841 * Total number of images: 14197122 * Number of images with bounding box annotations: 1,034,908 * Number of synsets with SIFT features: 1000 * Number of images with SIFT features: 1.2 million Source: ImageNet Large Scale Visual Recognition Challenge Image Source: https://cs.stanford.edu/people/karpathy/cnnembed/

BSD is a dataset used frequently for image denoising and super-resolution. Of the subdatasets, BSD100 is aclassical image dataset having 100 test images proposed by Martin et al.. The dataset is composed of a large variety of images ranging from natural images to object-specific such as plants, people, food etc. BSD100 is the testing set of the Berkeley segmentation dataset BSD300. Source: A Deep Journey into Super-resolution: A Survey Image Source: https://www.slideshare.net/jbhuang/single-image-super-resolution-from-transformed-selfexemplars-cvpr-2015

The Urban100 dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models. Image Source: http://vllab.ucmerced.edu/wlai24/LapSRN/

The Set14 dataset is a dataset consisting of 14 images commonly used for testing performance of Image Super-Resolution models. Image Source: https://www.ece.rice.edu/~wakin/images/

The Set5 dataset is a dataset consisting of 5 images (“baby”, “bird”, “butterfly”, “head”, “woman”) commonly used for testing performance of Image Super-Resolution models. Image Source: http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html

The fastMRI dataset includes two types of MRI scans: knee MRIs and the brain (neuro) MRIs, and containing training, validation, and masked test sets. The deidentified imaging dataset provided by NYU Langone comprises raw k-space data in several sub-dataset groups. Curation of these data are part of an IRB approved study. Raw and DICOM data have been deidentified via conversion to the vendor-neutral ISMRMD format and the RSNA clinical trial processor, respectively. Also, each DICOM image is manually inspected for the presence of any unexpected protected health information (PHI), with spot checking of both metadata and image content. Knee MRI: Data from more than 1,500 fully sampled knee MRIs obtained on 3 and 1.5 Tesla magnets and DICOM images from 10,000 clinical knee MRIs also obtained at 3 or 1.5 Tesla. The raw dataset includes coronal proton density-weighted images with and without fat suppression. The DICOM dataset contains coronal proton density-weighted with and without fat suppression, axial proton density-weighted with fat suppression, sagittal proton density, and sagittal T2-weighted with fat suppression. Brain MRI: Data from 6,970 fully sampled brain MRIs obtained on 3 and 1.5 Tesla magnets. The raw dataset includes axial T1 weighted, T2 weighted and FLAIR images. Some of the T1 weighted acquisitions included admissions of contrast agent. Source: https://fastmri.med.nyu.edu/ Image Source: https://fastmri.med.nyu.edu/

Manga109 has been compiled by the Aizawa Yamasaki Matsui Laboratory, Department of Information and Communication Engineering, the Graduate School of Information Science and Technology, the University of Tokyo. The compilation is intended for use in academic research on the media processing of Japanese manga. Manga109 is composed of 109 manga volumes drawn by professional manga artists in Japan. These manga were commercially made available to the public between the 1970s and 2010s, and encompass a wide range of target readerships and genres (see the table in Explore for further details.) Most of the manga in the compilation are available at the manga library “Manga Library Z” (formerly the “Zeppan Manga Toshokan” library of out-of-print manga). Source: Manga109 Image Source: https://arxiv.org/pdf/1510.04389v1.pdf

DRealSR establishes a Super Resolution (SR) benchmark with diverse real-world degradation processes, mitigating the limitations of conventional simulated image degradation. It has been collected from five DSLR cameras in natural scenes and cover indoor and outdoor scenes avoiding moving objects, e.g., advertising posters, plants, offices, buildings. The training images are cropped into 380×380, 272×272 and 192×192 patches, resulting in 31,970 patches. Source: Component Divide-and-Conquer for Real-World Image Super-Resolution

The General-100 dataset is a dataset for image super-resolution. It contains 100 bmp format images with no compression) The size of the 100 images ranges from 710 x 704 (large) to 131 x 112 (small).

Using the validation set (100 images) from the widely used DIV2K dataset, we blurred and subsampled each image with a different, randomly generated kernel. Kernels were 11x11 anisotropic gaussians with random lengths λ1, λ2∼U(0.6, 5) independently distributed for each axis, rotated by a random angle θ∼U[−π, π].

IXI Dataset is a collection of 600 MR brain images from normal, healthy subjects. The MR image acquisition protocol for each subject includes: * T1, T2 and PD-weighted images * MRA images * Diffusion-weighted images (15 directions) The data has been collected at three different hospitals in London: * Hammersmith Hospital using a Philips 3T system (details of scanner parameters) * Guy’s Hospital using a Philips 1.5T system (details of scanner parameters) * Institute of Psychiatry using a GE 1.5T system (details of the scan parameters not available at the moment) The data has been collected as part of the project: * IXI – Information eXtraction from Images (EPSRC GR/S21533/02) The images in NIFTI format can be downloaded from here: This data is made available under the Creative Commons CC BY-SA 3.0 license. If you use the IXI data please acknowledge the source of the IXI data.

The SKM-TEA dataset pairs raw quantitative knee MRI (qMRI) data, image data, and dense labels of tissues and pathology for end-to-end exploration and evaluation of the MR imaging pipeline. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient knee MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies. ## Challenge Tracks DICOM Track: The DICOM benchmarking track uses scanner-generated DICOM images as the input for image segmentation and detection tasks. Raw Data Track: The Raw Data benchmarking track uses raw MRI data (i.e. k-space) as the input for image reconstruction, segmentation and detection tasks.

Contains 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes. Source: CED: Color Event Camera Dataset

SEN12MS-CR is a multi-modal and mono-temporal data set for cloud removal. It contains observations covering 175 globally distributed Regions of Interest recorded in one of four seasons throughout the year of 2018. For each region, paired and co-registered synthetic aperture radar (SAR) Sentinel-1 measurements as well as cloudy and cloud-free optical multi-spectral Sentinel-2 observations from European Space Agency's Copernicus mission are provided. The Sentinel satellites provide public access data and are among the most prominent satellites in Earth observation. Source: Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery

Recently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of ~1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional reconstruction methods. This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms. Reference: Lyu, M., Mei, L., Huang, S. et al. M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research. Sci Data 10, 264 (2023). https://doi.org/10.1038/s41597-023-02181-4

SEN12MS-CR-TS is a multi-modal and multi-temporal data set for cloud removal. It contains time-series of paired and co-registered Sentinel-1 and cloudy as well as cloud-free Sentinel-2 data from European Space Agency's Copernicus mission. Each time series contains 30 cloudy and clear observations regularly sampled throughout the year 2018. Our multi-temporal data set is readily pre-processed and backward-compatible with SEN12MS-CR.

Ultra-high definition benchmark (UHDBench) includes 2293 images at 2k resolution sourced from the ground-truth test sets of HRSOD, LIU4k, UAVid, UHDM, and UHRSD. Images smaller than 2560 × 1440 are excluded.

Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka "2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", Sci Data 10, 576 (2023) or arXiv:2306.05907 (2023) Abstract: "Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline." The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by TESCAN-XRE NV, located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT) and 1536-by-1944 pixels, each. To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector. Between source and detector there is a rotation stage, upon which samples can be mounted. The machine components (i.e., the source, the detector panel, and the rotation stage) are mounted on translation belts that allow the moving of the components independently from one another. Please refer to the paper for all further technical details. The complete data collection can be found via the following links: 1-1,000, 1,001-2,000, 2,001-3,000, 3,001-4,000, 4,001-5,000, 5,521-6,370. Each slice folder ‘slice00001 - slice05000’ and ‘slice05521 - slice06370’ contains three folders for each mode: ‘mode1’, ‘mode2’, ‘mode3’. In each of these folders there are the sinogram, the dark-field, and the two flat-fields for the raw data archives, or just the reconstructions and for mode2 the additional reference segmentation. The corresponding reference reconstructions and segmentations can be found via the following links: 1-1,000, 1,001-2,000, 2,001-3,000, 3,001-4,000, 4,001-5,000, 5,521-6,370. The corresponding Python scripts for loading, pre-processing, reconstructing and segmenting the projection data in the way described in the paper can be found on github. A machine-readable file with the used scanning parameters and instrument data for each acquisition mode as well as a script loading it can be found on the GitHub repository as well. Note: It is advisable to use the graphical user interface when decompressing the .zip archives. If you experience a zipbomb error when unzipping the file on a Linux system rerun the command with the UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE environment variable by setting in your .bashrc “export UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE”. For more information or guidance in using the data collection, please get in touch with Maximilian.Kiss [at] cwi.nl Felix.Lucka [at] cwi.nl

An experimental and synthetic (simulated) OA raw signals and reconstructed image domain datasets rendered with different experimental parameters and tomographic acquisition geometries. For detailed information, see github.com/berkanlafci/oadat.

Used to show systematic performance improvement in applications such as high frame-rate video synthesis, feature/corner detection and tracking, as well as high dynamic range image reconstruction. Source: Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging

The current industrial pipeline includes 315 dynamic industrial scenarios, which can be categorized into three types: QR codes, text, and products. To enhance the diversity, we have different films with diverse material properties, coverage areas, film thicknesses, and levels of wrinkling. The film exhibits significant variability across each scenario. On the other hand, to ensure the stability of the industrial imaging pipeline, we maintained a consistent intensity level for the industrial light source and fixed the distance between the camera and the object flow. This helps to minimize the influence of errors external to the industrial system.

Overview The Spike-X4K Dataset is a high-resolution image reconstruction resource tailored for the latest advancements in spike camera technology. It is designed to meet the demands of modern spike cameras with a resolution of 1000×1000 pixels, surpassing the capabilities of previous datasets like spike-REDS, which was limited to a resolution of 250×400 pixels. ## Dataset Characteristics - Resolution: 1000×1000 pixels, aligning with state-of-the-art spike camera imaging standards. - Temporal Depth: The dataset captures the temporal dynamics of scenes with high-speed motion, providing a temporal sequence of spike frames. - Content: It includes both synthetic and real-world datasets, offering a diverse range of high-speed motion scenarios for training and testing image reconstruction models. - Pairs: The dataset comprises 1200 spike stream-ground truth image pairs for training and 45 pairs for testing, ensuring robust model evaluation. ## Motivation and Content Summary The development of the Spike-X4K Dataset was motivated by the need for a more representative and higher resolution dataset that could effectively train and evaluate image reconstruction models for spike cameras. Spike cameras, with their unique ability to capture photons independently at each pixel and generate binary spike streams, offer high temporal resolution and low latency, which are crucial for high-speed imaging. However, converting these spike streams into high-quality images requires sophisticated algorithms, and the Spike-X4K Dataset provides the necessary data to develop and refine these algorithms. ## Potential Use Cases 1. Algorithm Development: For researchers and engineers working on spike image reconstruction algorithms specific to spike camera data. 2. Benchmarking: As a standard for benchmarking the performance of various image reconstruction models against state-of-the-art techniques. 3. Training and Testing: Providing a large and diverse set of data for training deep learning models to handle high-speed motion imaging. 4. Feature Extraction: Enabling the study and improvement of feature extraction techniques from spike streams, including spatial and temporal features. 5. Spike Image Reconstruction Tasks: Supporting a wide range of computer vision tasks that can benefit from high-resolution, high-speed imaging, such as motion analysis, object tracking, and event detection.

This dataset is a collection of paired wireless signal data and corresponding image ground truth specifically designed for underground object sensing and image reconstruction. It utilizes Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) collected from a low-cost WiFi Wireless Sensor Network (WSN) deployed around a soil volume containing underground potato tubers. The data was acquired in a controlled laboratory environment using an automated system that ensures precise alignment between the wireless measurements and the physical location/shape of the target. The primary motivation for creating this dataset was to provide a resource for developing and evaluating non-destructive, low-cost methods for sensing underground biomass, overcoming the limitations of traditional techniques and simpler wireless metrics like RSSI. The dataset contains: - CSI amplitude (52 data subcarriers) and RSSI values from a 16-node ESP32-C3 WiFi mesh network across 14 channels, capturing frequency-dependent and overall signal attenuation for numerous links. For each wireless sample, a 360x360 pixel binary image mask representing the precise 2D cross-section of the underground potato tuber at that orientation, generated via automated camera and image segmentation. The dataset includes data collected from 8 different potato tubers, with measurements taken at 32 distinct rotation angles for each.

WiFiCam dataset for through-wall imaging based on WiFi channel state information. The corresponding source code repository is located at: https://github.com/StrohmayerJ/wificam Dataset Structure: /wificam ├── j3 └── 320 <-- 320x240 resolution subset └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3 └── csiComplex.npy <-- complex CSI sequence (cache) └── 92108.png <-- 320x240 RGB image └── 92112.png └── ... └── 640 <-- 640x480 resolution subset └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3 └── csiComplex.npy <-- complex CSI sequence (cache) └── 154.png <-- 640x480 RGB image └── 155.png └── ... ├── statistics320.csv <-- per-channel means and standard deviations for 320x240 images ├── statistics640.csv <-- per-channel means and standard deviations for 640x480 images

The scans are performed using a custom-built, highly flexible X-ray CT scanner, the FleX-ray scanner, developed by XRE nvand located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. The general purpose of the FleX-ray Lab is to conduct proof of concept experiments directly accessible to researchers in the field of mathematics and computer science. The scanner consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1536-by-1944 pixels, 14-bit flat panel detector (Dexella 1512NDT) and a rotation stage in-between, upon which a sample is mounted. All three components are mounted on translation stages which allow them to move independently from one another. Please refer to the paper for all further technical details. The complete data set can be found via the following links: 1-8 https://doi.org/10.5281/zenodo.2686725 , 9-16 https://doi.org/10.5281/zenodo.2686970, 17-24 https://doi.org/10.5281/zenodo.2687386, 25-32 https://doi.org/10.5281/zenodo.2687634, 33-37 https://doi.org/10.5281/zenodo.2687896, 38-42 https://doi.org/10.5281/zenodo.2688111 The corresponding Python scripts for loading, pre-processing and reconstructing the projection data in the way described in the paper can be found on github https://github.com/cicwi/WalnutReconstructionCodes

Description K-pop Idol Dataset - Female (KID-F) is the first dataset of K-pop idol high quality face images. It consists of about 6,000 high quality face images at 512x512 resolution and identity labels for each image. We collected about 90,000 K-pop female idol images and crop the face from each image. And we classified high quality face images. As a result, there are about 6,000 high quality face images in this dataset. There are 300 test datasets for a benchmark. There are no duplicate images between test and train images. Some identities in test images are not duplicated with train images. (It means some test images is new identity to the trained model) Each test images have its degraded pair. You can use these degraded test images for testing face super resolution performance. We also provide identity labels for each image. You can download the csv file from our github # Download You can download dataset from here. Google Drive # Agreement - The use of this software is RESTRICTED to non-commercial research and educational purposes. - All images of the KID-F dataset are obtained from the internet which are not property of EDA(PCEO-AI-CLUB). EDA is not responsible for the content nor the meaning of these images. - You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data. - You agree not to further copy, publish or distribute any portion of the KID-F dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset. - EDA reserves the right to terminate your access to the CelebA dataset at any time.