SIDD is an image denoising dataset containing 30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras. Ground truth images are provided along with the noisy images.
114 PAPERS • 1 BENCHMARK
Color BSD68 dataset for image denoising benchmarks is part of The Berkeley Segmentation Dataset and Benchmark. It is used for measuring image denoising algorithms performance. It contains 68 images.
69 PAPERS • 16 BENCHMARKS
Raindrop is a set of image pairs, where each pair contains exactly the same background scene, yet one is degraded by raindrops and the other one is free from raindrops. To obtain this, the images are captured through two pieces of exactly the same glass: one sprayed with water, and the other is left clean. The dataset consists of 1,119 pairs of images, with various background scenes and raindrops. They were captured with a Sony A6000 and a Canon EOS 60.
54 PAPERS • 1 BENCHMARK
Consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes.
37 PAPERS • 3 BENCHMARKS
The PIRM dataset consists of 200 images, which are divided into two equal sets for validation and testing. These images cover diverse contents, including people, objects, environments, flora, natural scenery, etc. Images vary in size, and are typically ~300K pixels in resolution.
31 PAPERS • 1 BENCHMARK
TinyPerson is a benchmark for tiny object detection in a long distance and with massive backgrounds. The images in TinyPerson are collected from the Internet. First, videos with a high resolution are collected from different websites. Second, images from the video are sampled every 50 frames. Then images with a certain repetition (homogeneity) are deleted, and the resulting images are annotated with 72,651 objects with bounding boxes by hand.
12 PAPERS • NO BENCHMARKS YET
A large-scale dataset of ~29.5K rain/rain-free image pairs that covers a wide range of natural rain scenes.
7 PAPERS • NO BENCHMARKS YET
Synthetic training set: This set is constructed in the following two steps and will be used for estimation/training purposes. i) 84,000 275 pixel x 400 pixel ground-truth fingerprint images without any noise or scratches, but with random transformations (at most five pixels translation and +/-10 degrees rotation) were generated by using the software Anguli: Synthetic Fingerprint Generator. ii) 84,000 275 pixel x 400 pixel degraded fingerprint images were generated by applying random artifacts (blur, brightness, contrast, elastic transformation, occlusion, scratch, resolution, rotation) and backgrounds to the ground-truth fingerprint images. In total, it contains 168,000 fingerprint images (84,000 fingerprints, and two impressions - one ground-truth and one degraded - per fingerprint).
1 PAPER • NO BENCHMARKS YET
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.
1 PAPER • NO BENCHMARKS YET