Image Super-Resolution

This dataset presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment. All sequences divided in training and testing are accessible through our website.

The airborne hyperspectral dataset was taken by Headwall Hyperspec-VNIR-C imaging sensor over agricultural and urban areas in Chikusei, Ibaraki, Japan, on July 29, 2014 between the times 9:56 to 10:53 UTC+9. The central point of the scene is located at coordinates: 36.294946N, 140.008380E. The hyperspectral dataset has 128 bands in the spectral range from 363 nm to 1018 nm. The scene consists of 2517x2335 pixels and the ground sampling distance was 2.5 m. Ground truth of 19 classes was collected via a field survey and visual inspection using high-resolution color images obtained by Canon EOS 5D Mark II together with the hyperspectral data. The hyperspectral data and ground truth were made available to the scientific community in the ENVI and MATLAB formats at http://park.itc.u-tokyo.ac.jp/sal/hyperdata. More details of the experiment are presented in the technical report given below. In order to use the datasets, please fulfill the following three requirements: 1) Giving an acknowledgement as follows: The authors gratefully acknowledge Space Application Laboratory, Department of Advanced Interdisciplinary Studies, the University of Tokyo for providing the hyperspectral data. 2) Using the following license for hyperspectral data: http://creativecommons.org/licenses/by/3.0/ 3) This dataset was made public by Dr. Naoto Yokoya and Prof. Akira Iwasaki from the University of Tokyo. Please cite: In WORD: N. Yokoya and A. Iwasaki, "Airborne hyperspectral data over Chikusei," Space Appl. Lab., Univ. Tokyo, Japan, Tech. Rep. SAL-2016-05-27, May 2016. In LaTex: @techreport{NYokoya2016, author = {N. Yokoya and A. Iwasaki}, title = {Airborne hyperspectral data over Chikusei}, institution = {Space Application Laboratory, University of Tokyo}, number = {SAL-2016-05-27}, address = {Japan}, month = {May}, year = 2016, }

A large-scale multi-scene dataset for stereo deblurring, containing 20,637 blurry-sharp stereo image pairs from 135 diverse sequences and their corresponding bidirectional disparities. Source: DAVANet: Stereo Deblurring with View Aggregation

EDFace-Celeb-1M is a public Ethnically Diverse Face dataset which is used to benchmark the task of face hallucination. The dataset includes 1.7 million photos that cover different countries, with balanced race composition.

Real-CE is a real-world Chinese-English benchmark dataset for the task of STISR with the emphasis on restoring structurally complex Chinese characters. The benchmark provides 1,935/783 real LR-HR text image pairs (contains 33,789 text lines in total) for training/testing in 2× and 4× zooming modes, complemented by detailed annotations, including detection boxes and text transcripts.

StereoMSI comprises of 350 registered colour-spectral image pairs. The dataset has been used for the two tracks of the PIRM2018 challenge. Source: PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study Image Source: Shoeiby et al

The Sentinel-2 satellite carries 12 CMOS detectors for the VNIR bands, with adjacent detectors having overlapping fields of view that result in overlapping regions in level-1 B (L1B) images. This dataset includes 3740 pairs of overlapping image crops extracted from two L1B products. Each crop has a height of around 400 pixels and a variable width that depends on the overlap width between detectors for RGBN bands, typically around 120-200 pixels. In addition to detector parallax, there is also cross-band parallax for each detector, resulting in shifts between bands. Pre-registration is performed for both cross-band and cross-detector parallax, with a precision of up to a few pixels (typically less than 10 pixels).

With the global availability of Multispectral Instrument (MSI, on-board Sentinel-2 satellite) data and Operational Land Imager (OLI, on-board Landsat-8 satellite) data which have 10 m and 30 m ground sample distance (GSD), respectively, it is conceivable that we can construct a dataset of LR-HR image pairs with OLI data as the LR image and MSI data as the HR image. This dataset, termed OLI2MSI, is a collection of paired real-world multisensor LR-HR data by carefully selecting relatively cloud-free MSI and OLI image in the same location obtained within a suitable temporal window.

SEN2VENµS is an open dataset for the super-resolution of Sentinel-2 images by leveraging simultaneous acquisitions with the VENµS satellite. The dataset is composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meters resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.

To construct such a dataset, a straightforward approach was scraping images from the web. The main source for our dataset is ShipSpotting, which serves as a repository for user uploaded images, hosting a vast collection of ship images, amounting to approximately 3 million. Furthermore, for each image, valuable supplementary information is available, such as the type of the ship, and present and past names. Next, we made sure that as many images as possible were collected in our dataset, since in deep learning, the quantity of training data directly influences the quality of results. A larger volume of data enables models to generalize more effectively. Thus we scrape all the images and as a result, the dataset comprises a total of 1.517.702 samples. We exclude many classes of ships from our final analysis and concentrate on the more common and valuable for a real scenario use case. The total number of different classes is 20 and the ship categories included are Bulkers, Containerships, Cruise ships, Dredgers, Fire Fighting Vessels, Floating Sheerlegs, General Cargo, Inland, Livestock Carriers, Passenger Vessels, Patrol Forces, Reefers, Ro-ro, Supply ships, Tankers, Training ships, Tugs, Vehicle Carriers, Wood Chip Carriers. The total amount of samples after this class selection is 507.918.

A collection of photographic and synthetic images intended for analysis of image processing techniques and quality assessment of displays. Image source: https://testimages.org/

To evaluate the performance on 4K burst images/video, we collect several clips from website. The dataset can be download from : https://drive.google.com/file/d/1YDljUONvyKUO24smTx__CUH_4Zxhle09/view?usp=sharing