The GoPro dataset for deblurring consists of 3,214 blurred images with the size of 1,280×720 that are divided into 2,103 training images and 1,111 test images. The dataset consists of pairs of a realistic blurry image and the corresponding ground truth shapr image that are obtained by a high-speed camera.
307 PAPERS • 3 BENCHMARKS
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.
80 PAPERS • 4 BENCHMARKS
Consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes.
68 PAPERS • 4 BENCHMARKS
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.
22 PAPERS • NO BENCHMARKS YET
The realistic and dynamic scenes (REDS) dataset was proposed in the NTIRE19 Challenge. The dataset is composed of 300 video sequences with resolution of 720×1,280, and each video has 100 frames, where the training set, the validation set and the testing set have 240, 30 and 30 videos, respectively
10 PAPERS • 2 BENCHMARKS
This dataset consists of blurred, noisy and defocused images.
1 PAPER • NO BENCHMARKS YET
YorkTag provides pairs of sharp/blurred images containing fiducial markers and is proposed to train and qualitatively and quantitatively evaluate our model.