We generate a synthesized dataset, namely VRDS, with 102 rainy videos from diverse scenarios, and each video frame has the corresponding rain streak map, raindrop mask, and the underlying rain-free clean image (ground truth). This dataset serves as a valuable resource for researchers in this field to develop and test novel methods for the removal of rain streaks and raindrops from video data. To enable our model to cope with various lighting conditions, we considered different weather scenarios, particularly cloudy conditions due to the close correlation between cloudy and rainy conditions. All of the scenarios are present in both the training and test sets, thereby allowing for fair and accurate comparisons between different methods on our dataset. We captured a total of 102 videos, 72 of which were used for training and 30 for testing. The selected video resolution is 1280$\times$720, and each contains 100 frames. Paper: Link Website: Github
BS-RSC is a real-world rolling shutter (RS) correction dataset and a corresponding model to correct the RS frames in a distorted video. Real distorted videos with corresponding ground truth are recorded simultaneously via a well-designed beam-splitter-based acquisition system. BSRSC contains various motions of both camera and objects in dynamic scenes.
A dataset of videos synthetically degraded with Adobe After Effects to exhibit artifacts resembling those of real-world analog videotapes. The original high-quality videos belong to the Venice scene of the Harmonic dataset. The artifacts taken into account are: 1) tape mistracking; 2) VHS edge waving; 3) chroma loss along the scanlines; 4) tape noise; 5) undersaturation. The dataset comprises a total of 26,392 frames corresponding to 40 clips. The clips are randomly divided into training and test sets with a 75%-25% ratio.
I2-2000FPS is the first high-speed video dataset offering an unprecedented temporal resolution of 2000 frames per second (fps). Captured using the commercially available Chronos 1.4 high-speed CMOS camera, the dataset includes a diverse range of objects varying in size, shape, orientation, and motion, as well as various camera movements. This dataset is designed to enable research in areas such as motion analysis, object tracking, and scene understanding at extreme temporal resolutions. Potential applications span fields like sports analysis, robotics, autonomous navigation, and high-speed videography.
SEPE 8K dataset is made of 40 different 8K (8192 x 4320) video sequences and 40 variant 8K (8192 x 5464) images. The video sequences were captured at a framerate of 29.97 frames per second (FPS) and had been encoded into videos using AVC/H.264, HEVC/H.265, and AV1 codecs at resolutions from 8K to 480p. The images, video sequences, encoded videos, and various other statistics related to the media that make the dataset are stored online, published, and maintained on the repo on GitHub for non-commercial use. this proposed dataset is - as far as we know - the first to publish true 8K natural sequences; thus, it is important for the next level of applications dealing with multimedia such as video quality assessment, super-resolution, video coding, video compression, and many more. Paper: https://doi.org/10.1145/3587819.3592560
Vident-lab is a dataset of dental videos with multi-task labels to facilitate further research in relevant video processing applications. The dataset constitutes a low-quality frame, its high-quality counterpart, a teeth segmentation mask, and an inter-frame homography matrix. The homography warps the current frame to the previous frame with respect to the teeth. The dataset has the training, validation, and test sets of 300, 29, and 80 videos, respectively. Source: Multi-task Video Enhancement for Dental Interventions Image Source: https://arxiv.org/pdf/2210.16236v1.pdf