{"url":"/dataset/based","name":"MSU BASED","full_name":"MSU BASED Video Deblurring Dataset and Benchmark","description_markdown":"Qualitative dataset with real blurred videos, created by using beam-splitter setup in lab environment","description_withheld":null,"homepage":"https://videoprocessing.ai/benchmarks/deblurring.html","introduced_date":"2022-10-24","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Deblurring","url":"/task/deblurring","datasets_with_task":"/datasets/task/deblurring"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MSU BASED"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/deblurring-on-based","task":"Deblurring","dataset_variant":"MSU BASED","rows":11,"metrics":["Subjective","SSIM","PSNR","VMAF","LPIPS","ERQAv2.0"],"first_row_in_archive_order":{"model":"NAFNet (REDS)","paper":"/paper/simple-baselines-for-image-restoration","metrics":{"ERQAv2.0":"0.74508","LPIPS":"0.08561","PSNR":"30.54803","SSIM":"0.95035","Subjective":"2.8405","VMAF":"66.85941"},"code_links":[{"title":"megvii-research/NAFNet","url":"https://github.com/megvii-research/NAFNet"},{"title":"murufeng/FUIR","url":"https://github.com/murufeng/FUIR"},{"title":"megvii-research/tlsc","url":"https://github.com/megvii-research/tlsc"},{"title":"megvii-research/TLC","url":"https://github.com/megvii-research/TLC"},{"title":"dhryougit/afm","url":"https://github.com/dhryougit/afm"},{"title":"rflepp/efficient_mobile_denoising_models","url":"https://github.com/rflepp/efficient_mobile_denoising_models"},{"title":"dhryougit/learning-to-translate-noise","url":"https://github.com/dhryougit/learning-to-translate-noise"},{"title":"dslisleedh/NAFNet-tensorflow2","url":"https://github.com/dslisleedh/NAFNet-tensorflow2"},{"title":"Thehunk1206/Image-Restorers","url":"https://github.com/Thehunk1206/Image-Restorers"},{"title":"setsunil/dsdnet","url":"https://github.com/setsunil/dsdnet"},{"title":"2023-MindSpore-4/Code-5","url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/MIMO-UNet"},{"title":"dslisleedh/NAFNet-flax","url":"https://github.com/dslisleedh/NAFNet-flax"},{"title":"lime-j/nafnet-jax","url":"https://github.com/lime-j/nafnet-jax"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/simple-baselines-for-image-restoration","title":"Simple Baselines for Image Restoration","date":"2022-04-10","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":30,"samples_ran":23,"samples_unverified":7,"pointer_only_for_licence":17,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/vrt-a-video-restoration-transformer","title":"VRT: A Video Restoration Transformer","date":"2022-01-28","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/maxim-multi-axis-mlp-for-image-processing","title":"MAXIM: Multi-Axis MLP for Image Processing","date":"2022-01-09","rows_on_this_dataset":2,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":46,"samples_ran":27,"samples_unverified":19,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/revisiting-global-statistics-aggregation-for","title":"Improving Image Restoration by Revisiting Global Information Aggregation","date":"2021-12-08","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/deep-residual-fourier-transformation-for","title":"Intriguing Findings of Frequency Selection for Image Deblurring","date":"2021-11-23","rows_on_this_dataset":2,"code_links":5,"syntology":null},{"paper":"/paper/restormer-efficient-transformer-for-high","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","date":"2021-11-18","rows_on_this_dataset":2,"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/deblurgan-v2-deblurring-orders-of-magnitude","title":"DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better","date":"2019-08-10","rows_on_this_dataset":1,"code_links":6,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":85,"samples_ran":58,"samples_unverified":27,"pointer_only_for_licence":26,"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."}