{"url":"/dataset/msu-vsr-benchmark","name":"MSU Video Super Resolution Benchmark: Detail Restoration","full_name":null,"description_markdown":"This is a dataset for a video super-resolution task. The dataset contains the most complex content for the restoration task: faces, text, QR-codes, car numbers, unpatterned textures, small details. Videos include different types of motion and different types of degradation: bicubic interpolation (BI) and Gaussian blurring and downsampling (BD). The resolution of all input video sequences is 480x320.\r\nSource: [https://videoprocessing.ai/benchmarks/video-super-resolution.html](https://videoprocessing.ai/benchmarks/video-super-resolution.html)\r\nImage Source: [https://videoprocessing.ai/benchmarks/video-super-resolution.html](https://videoprocessing.ai/benchmarks/video-super-resolution.html)","description_withheld":null,"homepage":"https://videoprocessing.ai/benchmarks/video-super-resolution.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Super-Resolution","url":"/task/video-super-resolution","datasets_with_task":"/datasets/task/video-super-resolution"}],"languages":[],"variants":["MSU Video Super Resolution Benchmark: Detail Restoration"],"data_loaders":[],"num_papers_in_archive":25,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-super-resolution-on-msu-vsr-benchmark","task":"Video Super-Resolution","dataset_variant":"MSU Video Super Resolution Benchmark: Detail Restoration","rows":32,"metrics":["Subjective score","ERQAv1.0","1 - LPIPS","SSIM","QRCRv1.0","PSNR","FPS"],"first_row_in_archive_order":{"model":"VRT","paper":"/paper/vrt-a-video-restoration-transformer","metrics":{"1 - LPIPS":"0.929","ERQAv1.0":"0.758","FPS":"2.778","PSNR":"31.669","QRCRv1.0":"0.722","SSIM":"0.902","Subjective score":"7.628"},"code_links":[{"title":"jingyunliang/vrt","url":"https://github.com/jingyunliang/vrt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/vrt-a-video-restoration-transformer","title":"VRT: A Video Restoration Transformer","date":"2022-01-28","rows_on_this_dataset":1,"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/swinir-image-restoration-using-swin","title":"SwinIR: Image Restoration Using Swin Transformer","date":"2021-08-23","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":45,"samples_ran":30,"samples_unverified":15,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hierarchical-conditional-flow-a-unified","title":"Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling","date":"2021-08-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/real-esrgan-training-real-world-blind-super","title":"Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data","date":"2021-07-22","rows_on_this_dataset":2,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/comisr-compression-informed-video-super","title":"COMISR: Compression-Informed Video Super-Resolution","date":"2021-05-04","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/temporal-modulation-network-for-controllable","title":"Temporal Modulation Network for Controllable Space-Time Video Super-Resolution","date":"2021-04-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":1,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-real-world-blind-face-restoration","title":"Towards Real-World Blind Face Restoration with Generative Facial Prior","date":"2021-01-11","rows_on_this_dataset":1,"code_links":1,"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/dynavsr-dynamic-adaptive-blind-video-super","title":"DynaVSR: Dynamic Adaptive Blind Video Super-Resolution","date":"2020-11-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/local-global-fusion-network-for-video-super","title":"Local-Global Fusion Network for Video Super-Resolution","date":"2020-09-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/revisiting-temporal-modeling-for-video-super","title":"Revisiting Temporal Modeling for Video Super-resolution","date":"2020-08-13","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/video-super-resolution-with-recurrent","title":"Video Super-Resolution with Recurrent Structure-Detail Network","date":"2020-08-02","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/blind-face-restoration-via-deep-multi-scale","title":"Blind Face Restoration via Deep Multi-scale Component Dictionaries","date":"2020-08-02","rows_on_this_dataset":1,"code_links":1,"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/video-super-resolution-with-temporal-group-1","title":"Video Super-resolution with Temporal Group Attention","date":"2020-07-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/iseebetter-spatio-temporal-video-super","title":"iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks","date":"2020-06-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/real-world-super-resolution-via-kernel","title":"Real-World Super-Resolution via Kernel Estimation and Noise Injection","date":"2020-06-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deformable-3d-convolution-for-video-super","title":"Deformable 3D Convolution for Video Super-Resolution","date":"2020-04-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-blind-video-super-resolution","title":"Deep Blind Video Super-resolution","date":"2020-03-10","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":9,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-video-super-resolution-using-hr-optical","title":"Deep Video Super-Resolution using HR Optical Flow Estimation","date":"2020-01-06","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/recurrent-back-projection-network-for-video","title":"Recurrent Back-Projection Network for Video Super-Resolution","date":"2019-03-25","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tdan-temporally-deformable-alignment-network","title":"TDAN: Temporally Deformable Alignment Network for Video Super-Resolution","date":"2018-12-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/esrgan-enhanced-super-resolution-generative","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","date":"2018-09-01","rows_on_this_dataset":1,"code_links":46,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":44,"samples_ran":8,"samples_unverified":36,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-video-super-resolution-network-using","title":"Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation","date":"2018-06-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/learning-a-single-convolutional-super","title":"Learning a Single Convolutional Super-Resolution Network for Multiple Degradations","date":"2017-12-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/real-time-single-image-and-video-super","title":"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network","date":"2016-09-16","rows_on_this_dataset":1,"code_links":47,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":4,"samples_unverified":16,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":13,"samples_harvested":168,"samples_ran":68,"samples_unverified":100,"pointer_only_for_licence":14,"papers_with_no_sample_that_ran":3,"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."}