{"url":"/dataset/gopro","name":"GoPro","full_name":null,"description_markdown":"The **GoPro** dataset for deblurring consists of 3,214 blurred images with the size of 1,280×720 that are divided into 2,103 training images and 1,111 test images. The dataset consists of pairs of a realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera.\r\n\r\nSource: [Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networksfor Non-Uniform Single Image Deblurring](https://arxiv.org/abs/1903.10157)\r\nImage Source: [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://openaccess.thecvf.com/content_cvpr_2017/papers/Nah_Deep_Multi-Scale_Convolutional_CVPR_2017_paper.pdf)","description_withheld":null,"homepage":"https://seungjunnah.github.io/Datasets/gopro","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-multi-scale-convolutional-neural-network","title":"Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring","first_author":"Seungjun Nah","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Deblurring","url":"/task/deblurring","datasets_with_task":"/datasets/task/deblurring"},{"name":"Video Frame Interpolation","url":"/task/video-frame-interpolation","datasets_with_task":"/datasets/task/video-frame-interpolation"},{"name":"Image Deblurring","url":"/task/image-deblurring","datasets_with_task":"/datasets/task/image-deblurring"},{"name":"Unified Image Restoration","url":"/task/unified-image-restoration","datasets_with_task":"/datasets/task/unified-image-restoration"}],"languages":[],"variants":["GoPro linear subset","GoPro"],"data_loaders":[{"repo":null,"url":"","frameworks":["pytorch"]}],"num_papers_in_archive":390,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/deblurring-on-gopro","task":"Deblurring","dataset_variant":"GoPro","rows":56,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"BSSTNet","paper":"/paper/blur-aware-spatio-temporal-sparse-transformer","metrics":{"PSNR":"35.98","SSIM":"0.9792"},"code_links":[{"title":"huicongzhang/bsstnet","url":"https://github.com/huicongzhang/bsstnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-deblurring-on-gopro","task":"Image Deblurring","dataset_variant":"GoPro","rows":55,"metrics":["PSNR","SSIM","Params (M)","FID","LPIPS"],"first_row_in_archive_order":{"model":"AdaRevD","paper":"/paper/adarevd-adaptive-patch-exiting-reversible-1","metrics":{"PSNR":"34.6","SSIM":"0.972"},"code_links":[{"title":"invokerer/deeprft","url":"https://github.com/invokerer/deeprft"},{"title":"INVOKERer/AdaRevD","url":"https://github.com/INVOKERer/AdaRevD"},{"title":"INVOKERer/LoFormer","url":"https://github.com/INVOKERer/LoFormer"},{"title":"deepmed-lab-ecnu/single-image-deblur","url":"https://github.com/deepmed-lab-ecnu/single-image-deblur"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/video-frame-interpolation-on-gopro","task":"Video Frame Interpolation","dataset_variant":"GoPro","rows":2,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"DBVI","paper":"/paper/deep-bayesian-video-frame-interpolation","metrics":{"PSNR":"31.73","SSIM":"0.947"},"code_links":[{"title":"Oceanlib/DBVI","url":"https://github.com/Oceanlib/DBVI"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unified-image-restoration-on-gopro","task":"Unified Image Restoration","dataset_variant":"GoPro","rows":1,"metrics":["Average PSNR (dB)"],"first_row_in_archive_order":{"model":"DA-RCOT","paper":"/paper/degradation-aware-residual-conditioned","metrics":{"Average PSNR (dB)":"28.68"},"code_links":[{"title":"xl-tang3/RCOT","url":"https://github.com/xl-tang3/RCOT"},{"title":"xl-tang3/DA-RCOT","url":"https://github.com/xl-tang3/DA-RCOT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-polarization-aided-transformer-for-image","title":"A Polarization-Aided Transformer for Image Deblurring via Motion Vector Decomposition","date":"2025-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mair-a-locality-and-continuity-preserving","title":"MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration","date":"2024-12-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/xyscannet-an-interpretable-state-space-model","title":"XYScanNet: A State Space Model for Single Image Deblurring","date":"2024-12-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hierarchical-information-flow-for-generalized","title":"Hierarchical Information Flow for Generalized Efficient Image Restoration","date":"2024-11-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/degradation-aware-residual-conditioned","title":"Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration","date":"2024-11-03","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":6,"samples_unverified":0,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/loformer-local-frequency-transformer-for","title":"LoFormer: Local Frequency Transformer for Image Deblurring","date":"2024-07-24","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/instruct-ipt-all-in-one-image-processing-1","title":"Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation","date":"2024-06-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/revitalizing-convolutional-network-for-image","title":"Revitalizing Convolutional Network for Image Restoration","date":"2024-06-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adarevd-adaptive-patch-exiting-reversible-1","title":"AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring","date":"2024-06-13","rows_on_this_dataset":2,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":50,"samples_ran":36,"samples_unverified":14,"pointer_only_for_licence":50,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/blur-aware-spatio-temporal-sparse-transformer","title":"Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring","date":"2024-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":10,"samples_unverified":7,"pointer_only_for_licence":17,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficient-visual-state-space-model-for-image","title":"Efficient Visual State Space Model for Image Deblurring","date":"2024-05-23","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/aggregating-local-and-global-features-via","title":"Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring","date":"2024-03-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deblurdinat-a-lightweight-and-effective","title":"DeblurDiNAT: A Compact Model with Exceptional Generalization and Visual Fidelity on Unseen Domains","date":"2024-03-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cascadedgaze-efficiency-in-global-context","title":"CascadedGaze: Efficiency in Global Context Extraction for Image Restoration","date":"2024-01-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":10,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mr-vnet-media-restoration-using-volterra","title":"MR-VNet: Media Restoration using Volterra Networks","date":"2024-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/efficient-multi-scale-network-with-learnable","title":"Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring","date":"2023-12-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":9,"samples_unverified":3,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/exploring-the-potential-of-channel","title":"Exploring the potential of channel interactions for image restoration","date":"2023-12-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/id-blau-image-deblurring-by-implicit","title":"ID-Blau: Image Deblurring by Implicit Diffusion-based reBLurring AUgmentation","date":"2023-12-18","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/image-restoration-via-frequency-selection","title":"Image Restoration via Frequency Selection","date":"2023-11-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/prompt-based-all-in-one-image-restoration","title":"Prompt-based Ingredient-Oriented All-in-One Image Restoration","date":"2023-09-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":11,"samples_unverified":1,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deformable-convolutions-and-lstm-based","title":"Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurring","date":"2023-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/neural-image-re-exposure","title":"Neural Image Re-Exposure","date":"2023-05-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-mountain-shaped-single-stage-network-for","title":"A Mountain-Shaped Single-Stage Network for Accurate Image Restoration","date":"2023-05-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/irnext-rethinking-convolutional-network","title":"IRNeXt: Rethinking Convolutional Network Design for Image Restoration","date":"2023-04-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/selective-frequency-network-for-image","title":"Selective Frequency Network for Image Restoration","date":"2023-04-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficient-and-explicit-modelling-of-image","title":"Efficient and Explicit Modelling of Image Hierarchies for Image Restoration","date":"2023-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":4,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mixed-hierarchy-network-for-image-restoration","title":"Mixed Hierarchy Network for Image Restoration","date":"2023-02-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/revisiting-image-deblurring-with-an-efficient","title":"Revisiting Image Deblurring with an Efficient ConvNet","date":"2023-02-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/image-restoration-with-mean-reverting","title":"Image Restoration with Mean-Reverting Stochastic Differential Equations","date":"2023-01-27","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":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/self-supervised-non-uniform-kernel-estimation","title":"Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/efficient-frequency-domain-based-transformers","title":"Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring","date":"2022-11-22","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":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-bayesian-video-frame-interpolation","title":"Deep Bayesian Video Frame Interpolation","date":"2022-10-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-outputs-is-all-you-need-for-deblur","title":"Multi-Outputs Is All You Need For Deblur","date":"2022-08-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-degradation-representations-for","title":"Learning Degradation Representations for Image Deblurring","date":"2022-08-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":7,"samples_unverified":4,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spatio-temporal-deformable-attention-network","title":"Spatio-Temporal Deformable Attention Network for Video Deblurring","date":"2022-07-22","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/no-attention-is-needed-grouped-spatial","title":"A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift","date":"2022-06-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-flow-aligned-sequence-to","title":"Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration","date":"2022-05-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/stripformer-strip-transformer-for-fast-image","title":"Stripformer: Strip Transformer for Fast Image Deblurring","date":"2022-04-10","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/simple-baselines-for-image-restoration","title":"Simple Baselines for Image Restoration","date":"2022-04-10","rows_on_this_dataset":2,"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/mssnet-multi-scale-stage-network-for-single","title":"MSSNet: Multi-Scale-Stage Network for Single Image Deblurring","date":"2022-02-19","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"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/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/flow-guided-sparse-transformer-for-video","title":"Flow-Guided Sparse Transformer for Video Deblurring","date":"2022-01-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-recurrent-neural-network-with-multi","title":"Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring","date":"2021-12-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":12,"samples_unverified":2,"pointer_only_for_licence":14,"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":7,"code_links":3,"syntology":null},{"paper":"/paper/mefnet-multi-scale-event-fusion-network-for","title":"Event-Based Fusion for Motion Deblurring with Cross-modal Attention","date":"2021-11-30","rows_on_this_dataset":1,"code_links":1,"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/sdwnet-a-straight-dilated-network-with","title":"SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring","date":"2021-10-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ghost-deblurgan-and-its-application-to","title":"Application of Ghost-DeblurGAN to Fiducial Marker Detection","date":"2021-09-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-coarse-to-fine-approach-in-single","title":"Rethinking Coarse-to-Fine Approach in Single Image Deblurring","date":"2021-08-11","rows_on_this_dataset":2,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":1,"samples_unverified":8,"pointer_only_for_licence":6,"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":2,"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/uformer-a-general-u-shaped-transformer-for","title":"Uformer: A General U-Shaped Transformer for Image Restoration","date":"2021-06-06","rows_on_this_dataset":2,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":6,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hinet-half-instance-normalization-network-for","title":"HINet: Half Instance Normalization Network for Image Restoration","date":"2021-05-13","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/multi-stage-progressive-image-restoration","title":"Multi-Stage Progressive Image Restoration","date":"2021-02-04","rows_on_this_dataset":2,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":26,"samples_ran":18,"samples_unverified":8,"pointer_only_for_licence":25,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/banet-blur-aware-attention-networks-for","title":"BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring","date":"2021-01-19","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/blur-more-to-deblur-better-multi-blur2deblur","title":"Blur More To Deblur Better: Multi-Blur2Deblur For Efficient Video Deblurring","date":"2020-12-23","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/flavr-flow-agnostic-video-representations-for","title":"FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation","date":"2020-12-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/motion-aware-double-attention-network-for","title":"Motion Aware Double Attention Network for Dynamic Scene Deblurring","date":"2020-06-19","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/dark-and-bright-channel-prior-embedded","title":"Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring","date":"2020-05-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/learning-event-based-motion-deblurring","title":"Learning Event-Based Motion Deblurring","date":"2020-04-13","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/spatially-attentive-patch-hierarchical","title":"Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring","date":"2020-04-11","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"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/deblurring-by-realistic-blurring","title":"Deblurring by Realistic Blurring","date":"2020-04-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/multi-temporal-recurrent-neural-networks-for","title":"Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training","date":"2019-11-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"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":6,"code_links":6,"syntology":null},{"paper":"/paper/dynamic-scene-deblurring-with-parameter","title":"Dynamic Scene Deblurring With Parameter Selective Sharing and Nested Skip Connections","date":"2019-06-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/spatio-temporal-filter-adaptive-network-for","title":"Spatio-Temporal Filter Adaptive Network for Video Deblurring","date":"2019-04-28","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":4,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-stacked-hierarchical-multi-patch-network","title":"Deep Stacked Hierarchical Multi-patch Network for Image Deblurring","date":"2019-04-06","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/spatially-adaptive-residual-networks-for","title":"Motion Deblurring with an Adaptive Network","date":"2019-03-25","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/bringing-alive-blurred-moments","title":"Bringing Alive Blurred Moments","date":"2018-04-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/scale-recurrent-network-for-deep-image","title":"Scale-recurrent Network for Deep Image Deblurring","date":"2018-02-06","rows_on_this_dataset":2,"code_links":4,"syntology":null},{"paper":"/paper/deep-multi-scale-convolutional-neural-network","title":"Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring","date":"2016-12-07","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":0,"samples_unverified":11,"pointer_only_for_licence":0,"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":29,"samples_harvested":344,"samples_ran":231,"samples_unverified":113,"pointer_only_for_licence":193,"papers_with_no_sample_that_ran":1,"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."}