{"url":"/dataset/vrds","name":"VRDS","full_name":"Video Raindrop and Rain Streak Removal","description_markdown":"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.\r\nAll 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.\r\nWe 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. \r\n\r\nPaper: [Link](https://dl.acm.org/doi/abs/10.1145/3581783.3612001)\r\n\r\nWebsite: [Github](https://github.com/TonyHongtaoWu/ViMP-Net)","description_withheld":null,"homepage":"https://github.com/TonyHongtaoWu/ViMP-Net","introduced_date":"2023-10-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Rain Removal","url":"/task/rain-removal","datasets_with_task":"/datasets/task/rain-removal"},{"name":"Video Restoration","url":"/task/video-restoration","datasets_with_task":"/datasets/task/video-restoration"},{"name":"Video deraining","url":"/task/video-deraining","datasets_with_task":"/datasets/task/video-deraining"},{"name":"Raindrop Removal","url":"/task/raindrop-removal","datasets_with_task":"/datasets/task/raindrop-removal"}],"languages":[],"variants":["VRDS"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-deraining-on-vrds","task":"Video deraining","dataset_variant":"VRDS","rows":8,"metrics":["SSIM","PSNR"],"first_row_in_archive_order":{"model":"Turtle","paper":"/paper/learning-truncated-causal-history-model-for","metrics":{"PSNR":"32.01","SSIM":"0.9590"},"code_links":[{"title":"Ascend-Research/Turtle","url":"https://github.com/Ascend-Research/Turtle"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learning-truncated-causal-history-model-for","title":"Learning Truncated Causal History Model for Video Restoration","date":"2024-10-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":7,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rainmamba-enhanced-locality-learning-with-2","title":"RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining","date":"2024-07-31","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/recurrent-video-restoration-transformer-with","title":"Recurrent Video Restoration Transformer with Guided Deformable Attention","date":"2022-06-05","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":7,"samples_unverified":7,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-trajectory-aware-transformer-for","title":"Learning Trajectory-Aware Transformer for Video Super-Resolution","date":"2022-04-08","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":1,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/restormer-efficient-transformer-for-high","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","date":"2021-11-18","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/basicvsr-improving-video-super-resolution","title":"BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment","date":"2021-04-27","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/basicvsr-the-search-for-essential-components","title":"BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond","date":"2020-12-03","rows_on_this_dataset":1,"code_links":6,"syntology":null},{"paper":"/paper/mprnet-multi-path-residual-network-for","title":"MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution","date":"2020-11-09","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":39,"samples_ran":21,"samples_unverified":18,"pointer_only_for_licence":9,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}