The dataset consists of 4,738 pairs of images of 232 different scenes including reference pairs. All images were captured both in the camera raw and JPEG formats, hence generating two datasets: RealBlur-R from the raw images, and RealBlur-J from the JPEG images. Each training set consists of 3,758 image pairs, while each test set consists of 980 image pairs. The deblurring result is first aligned to its ground truth sharp image using a homography estimated by the enhanced correlation coefficients method, and PSNR or SSIM is computed in sRGB color space.
This dataset focus on two blur types: camera motion blur and defocus blur. For each type of blur we synthesize $5$ scenes using Blender. We manually place multi-view cameras to mimic real data capture. To render images with camera motion blur, we randomly perturb the camera pose, and then linearly interpolate poses between the original and perturbed poses for each view. We render images from interpolated poses and blend them in linear RGB space to generate the final blurry images. For defocus blur, we use the built-in functionality to render depth-of-field images. We fix the aperture and randomly choose a focus plane between the nearest and furthest depth. We also captured $20$ real world scenes with $10$ scenes for each blur type for a qualitative study. The camera used was a Canon EOS RP with manual exposure mode. We captured the camera motion blur images by manually shaking the camera during exposure, while the reference images are taken using a tripod. To capture defocus images, we choose a large aperture. We compute the camera poses of blurry and reference images in the real world scenes using the COLMAP.
The Dialog State Tracking Challenges 2 & 3 (DSTC2&3) were research challenge focused on improving the state of the art in tracking the state of spoken dialog systems. State tracking, sometimes called belief tracking, refers to accurately estimating the user's goal as a dialog progresses. Accurate state tracking is desirable because it provides robustness to errors in speech recognition, and helps reduce ambiguity inherent in language within a temporal process like dialog. In these challenges, participants were given labelled corpora of dialogs to develop state tracking algorithms. The trackers were then evaluated on a common set of held-out dialogs, which were released, un-labelled, during a one week period. The corpus was collected using Amazon Mechanical Turk, and consists of dialogs in two domains: restaurant information, and tourist information. Tourist information subsumes restaurant information, and includes bars, cafés etc. as well as multiple new slots. There were two rounds of evaluation using this data: DSTC 2 released a large number of training dialogs related to restaurant search. Compared to DSTC (which was in the bus timetables domain), DSTC 2 introduces changing user goals, tracking 'requested slots' as well as the new restaurants domain. Results from DSTC 2 were presented at SIGDIAL 2014. DSTC 3 addressed the problem of adaption to a new domain - tourist information. DSTC 3 releases a small amount of labelled data in the tourist information domain; participants will use this data plus the restaurant data from DSTC 2 for training. Dialogs used for training are fully labelled; user transcriptions, user dialog-act semantics and dialog state are all annotated. (This corpus therefore is also suitable for studies in Spoken Language Understanding.) Source: https://github.com/matthen/dstc Image Source: https://www.aclweb.org/anthology/W13-4065.pdf
The RSBlur dataset provides pairs of real and synthetic blurred images with ground truth sharp images. The dataset enables the evaluation of deblurring methods and blur synthesis methods on real-world blurred images. Training, validation, and test sets consist of 8,878, 1,120, and 3,360 blurred images, respectively.
QMUL-SurvFace is a surveillance face recognition benchmark that contains 463,507 face images of 15,573 distinct identities captured in real-world uncooperative surveillance scenes over wide space and time. Source: Surveillance Face Recognition Challenge
This is a gun detection dataset with 51K annotated gun images for gun detection and other 51K cropped gun chip images for gun classification collected from a few different sources.
A dataset of over 65,000 pairs of incorrectly white-balanced images and their corresponding correctly white-balanced images. Source: When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images
Using the proposed beam-splitter acquisition system, we have collected a new real-world video deblurring dataset (BSD). We collected blurry/sharp video sequences for three different blur intensity settings (sharp exposure time – blurry exposure time), 1ms–8ms, 2ms–16ms, and 3ms–24ms(most blur), respectively. Github
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
This dataset consists of blurred, noisy and defocused images. ### Introduction Dataset consists of blurred images captured using mobile phones in real-world scenario. Images were captured under wide variety of lighting conditions, weather, indoor and outdoor. This dataset can be used for Image De-noising, Deblurring and noise removal algorithms. This can be also work as robust test set for denoising algorithms. ### Dataset Features - Captured by 3000+ unique users - Rich in diversity - Mobile phone view point - HD Resolution - Various lighting conditions - Indoor and Outdoor scene ### Dataset Features - Classification and detection annotations available - Multiple category annotations possible - COCO, PASCAL VOC and YOLO formats To download full datasets or to submit a request for your dataset needs, please ping us on sales@datacluster.ai Visit www.datacluster.in to know more. Note: All the images are manually verified and are contributed by the large contributor base on DataCluster platform
The DAVIDE dataset consists of synchronized blurred, depth, and sharp videos. The dataset comprises 90 video sequences divided into 69 for training, 7 for validation, and 14 for testing. The test set includes annotations of seven content attributes categorized by: 1) environment (indoor/outdoor), 2) motion (camera motion/camera and object motion), and 3) scene proximity (close/mid/far). These annotations aim to facilitate further analysis into scenarios where depth information could be more beneficial.
Contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground. Over 160,000 annotated frames forhundreds of ImageNet classes are available, which are used for baseline experiments that assess the impact of known and unknown image artifacts and other conditions on common deep learning-based object classification approaches. Source: UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
YorkTag provides pairs of sharp/blurred images containing fiducial markers and is proposed to train and qualitatively and quantitatively evaluate our model.