{"url":"/dataset/beam-splitter-deblurring-bsd","name":"Beam-Splitter Deblurring (BSD)","full_name":"3ms-24ms","description_markdown":"Using the proposed beam-splitter acquisition system, we have collected a new real-world video deblurring dataset (BSD).\r\n\r\nWe 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.\r\n\r\n[Github](https://github.com/zzh-tech/ESTRNN)","description_withheld":null,"homepage":"https://github.com/zzh-tech/ESTRNN","introduced_date":"2021-06-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/efficient-spatio-temporal-recurrent-neural-1","title":"Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network","first_author":"Zhihang Zhong","url":null},"license":null,"modalities":[],"tasks":[{"name":"Deblurring","url":"/task/deblurring","datasets_with_task":"/datasets/task/deblurring"},{"name":"Video Deblurring","url":"/task/video-deblurring","datasets_with_task":"/datasets/task/video-deblurring"}],"languages":[],"variants":["Beam-Splitter Deblurring (BSD)"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/deblurring-on-beam-splitter-deblurring-bsd","task":"Deblurring","dataset_variant":"Beam-Splitter Deblurring (BSD)","rows":5,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Turtle","paper":"/paper/learning-truncated-causal-history-model-for","metrics":{"PSNR":"33.58"},"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/recurrent-video-deblurring-with-blur","title":"Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes","date":"2021-08-23","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficient-spatio-temporal-recurrent-neural-1","title":"Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network","date":"2021-06-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":7,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cascaded-deep-video-deblurring-using-temporal","title":"Cascaded Deep Video Deblurring Using Temporal Sharpness Prior","date":"2020-04-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-video-deblurring-for-hand-held-cameras","title":"Deep Video Deblurring for Hand-Held Cameras","date":"2017-07-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":28,"samples_ran":20,"samples_unverified":8,"pointer_only_for_licence":2,"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."}