{"url":"/dataset/urban100","name":"Urban100","full_name":null,"description_markdown":"The **Urban100** dataset contains 100 images of urban scenes. It commonly used as a test set to evaluate the performance of super-resolution models.\r\nImage Source: [http://vllab.ucmerced.edu/wlai24/LapSRN/](http://vllab.ucmerced.edu/wlai24/LapSRN/)","description_withheld":null,"homepage":"https://github.com/jbhuang0604/SelfExSR","introduced_date":"2015-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/single-image-super-resolution-from","title":"Single Image Super-Resolution From Transformed Self-Exemplars","first_author":"Jia-Bin Huang","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Super-Resolution","url":"/task/image-super-resolution","datasets_with_task":"/datasets/task/image-super-resolution"},{"name":"Color Image Denoising","url":"/task/color-image-denoising","datasets_with_task":"/datasets/task/color-image-denoising"},{"name":"Grayscale Image Denoising","url":"/task/grayscale-image-denoising","datasets_with_task":"/datasets/task/grayscale-image-denoising"},{"name":"Image Denoising","url":"/task/image-denoising","datasets_with_task":"/datasets/task/image-denoising"},{"name":"Blind Super-Resolution","url":"/task/blind-super-resolution","datasets_with_task":"/datasets/task/blind-super-resolution"},{"name":"Joint Demosaicing and Denoising","url":"/task/joint-demosaicing-and-denoising","datasets_with_task":"/datasets/task/joint-demosaicing-and-denoising"},{"name":"Compressive Sensing","url":"/task/compressive-sensing","datasets_with_task":"/datasets/task/compressive-sensing"}],"languages":[],"variants":["Urban100","Urban100 sigma70","Urban100 sigma50","Urban100 sigma30","Urban100 sigma25","urban100 sigma15","Urban100 sigma10","Urban100 - 8x upscaling","Urban100 - 4x upscaling","Urban100 - 3x upscaling","Urban100 - 2x upscaling","Urban100 - 16x upscaling"],"data_loaders":[{"repo":"https://github.com/jbhuang0604/SelfExSR","url":"https://github.com/jbhuang0604/SelfExSR","frameworks":[]},{"repo":"https://github.com/eugenesiow/super-image-data","url":"https://github.com/eugenesiow/super-image-data","frameworks":["pytorch"]}],"num_papers_in_archive":591,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-super-resolution-on-urban100-4x","task":"Image Super-Resolution","dataset_variant":"Urban100 - 4x upscaling","rows":65,"metrics":["PSNR","SSIM","LPIPS","Perceptual Index","DISTS","LR-PSNR"],"first_row_in_archive_order":{"model":"Hi-IR-L","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"28.72","SSIM":"0.8514"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-super-resolution-on-urban100-2x","task":"Image Super-Resolution","dataset_variant":"Urban100 - 2x upscaling","rows":29,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"HMA†","paper":"/paper/hmanet-hybrid-multi-axis-aggregation-network","metrics":{"PSNR":"35.24","SSIM":"0.9513"},"code_links":[{"title":"korouuuuu/hma","url":"https://github.com/korouuuuu/hma"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-super-resolution-on-urban100-3x","task":"Image Super-Resolution","dataset_variant":"Urban100 - 3x upscaling","rows":22,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR-L","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"31.07","SSIM":"0.902"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/grayscale-image-denoising-on-urban100-sigma25","task":"Grayscale Image Denoising","dataset_variant":"Urban100 sigma25","rows":10,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"31.92"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/grayscale-image-denoising-on-urban100-sigma50","task":"Grayscale Image Denoising","dataset_variant":"Urban100 sigma50","rows":10,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"28.91"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-urban100-sigma50","task":"Color Image Denoising","dataset_variant":"Urban100 sigma50","rows":9,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"30.59"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-urban100-sigma15-1","task":"Color Image Denoising","dataset_variant":"urban100 sigma15","rows":8,"metrics":["Average PSNR","PSNR"],"first_row_in_archive_order":{"model":"AKDT","paper":"/paper/akdt-adaptive-kernel-dilation-transformer-for","metrics":{"Average PSNR":"35.63","PSNR":"35.63"},"code_links":[{"title":"albrateanu/AKDT","url":"https://github.com/albrateanu/AKDT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/grayscale-image-denoising-on-urban100-sigma15","task":"Grayscale Image Denoising","dataset_variant":"Urban100 sigma15","rows":7,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Restormer","paper":"/paper/restormer-efficient-transformer-for-high","metrics":{"PSNR":"33.79"},"code_links":[{"title":"swz30/restormer","url":"https://github.com/swz30/restormer"},{"title":"swz30/MPRNet","url":"https://github.com/swz30/MPRNet"},{"title":"swz30/MIRNet","url":"https://github.com/swz30/MIRNet"},{"title":"swz30/CycleISP","url":"https://github.com/swz30/CycleISP"},{"title":"swz30/mirnetv2","url":"https://github.com/swz30/mirnetv2"},{"title":"leftthomas/restormer","url":"https://github.com/leftthomas/restormer"},{"title":"MKFMIKU/VIDM","url":"https://github.com/MKFMIKU/VIDM"},{"title":"stephen0808/dnlut","url":"https://github.com/stephen0808/dnlut"},{"title":"HDCVLab/MC-Blur-Dataset","url":"https://github.com/HDCVLab/MC-Blur-Dataset"},{"title":"GarrickZ2/Image-Denoising","url":"https://github.com/GarrickZ2/Image-Denoising"},{"title":"txyugood/Restormer_Paddle","url":"https://github.com/txyugood/Restormer_Paddle"},{"title":"gymoon10/Instance-Segmentation-with-SpatialEmbedding-CA","url":"https://github.com/gymoon10/Instance-Segmentation-with-SpatialEmbedding-CA"},{"title":"prakashSidd18/blind_augmentation","url":"https://github.com/prakashSidd18/blind_augmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-urban100-sigma25","task":"Color Image Denoising","dataset_variant":"Urban100 sigma25","rows":6,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"33.34"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-super-resolution-on-urban100-8x","task":"Image Super-Resolution","dataset_variant":"Urban100 - 8x upscaling","rows":5,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"DRLN+","paper":"/paper/densely-residual-laplacian-super-resolution","metrics":{"PSNR":"23.24","SSIM":"0.6523"},"code_links":[{"title":"saeed-anwar/DRLN","url":"https://github.com/saeed-anwar/DRLN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-denoising-on-urban100-sigma15","task":"Image Denoising","dataset_variant":"urban100 sigma15","rows":4,"metrics":["Average PSNR","PSNR"],"first_row_in_archive_order":{"model":"AKDT","paper":"/paper/akdt-adaptive-kernel-dilation-transformer-for","metrics":{"Average PSNR":"35.64"},"code_links":[{"title":"albrateanu/AKDT","url":"https://github.com/albrateanu/AKDT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-denoising-on-urban100-sigma50","task":"Image Denoising","dataset_variant":"Urban100 sigma50","rows":4,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"MaIR+","paper":"/paper/mair-a-locality-and-continuity-preserving","metrics":{"PSNR":"30.41"},"code_links":[{"title":"XLearning-SCU/2025-CVPR-MaIR","url":"https://github.com/XLearning-SCU/2025-CVPR-MaIR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/blind-super-resolution-on-urban100-2x","task":"Blind Super-Resolution","dataset_variant":"Urban100 - 2x upscaling","rows":3,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"DCLS","paper":"/paper/deep-constrained-least-squares-for-blind","metrics":{"PSNR":"31.69","SSIM":"0.9202"},"code_links":[{"title":"megvii-research/dcls-sr","url":"https://github.com/megvii-research/dcls-sr"},{"title":"algolzw/dcls","url":"https://github.com/algolzw/dcls"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/blind-super-resolution-on-urban100-4x","task":"Blind Super-Resolution","dataset_variant":"Urban100 - 4x upscaling","rows":3,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"DCLS","paper":"/paper/deep-constrained-least-squares-for-blind","metrics":{"PSNR":"26.15","SSIM":"0.7809"},"code_links":[{"title":"megvii-research/dcls-sr","url":"https://github.com/megvii-research/dcls-sr"},{"title":"algolzw/dcls","url":"https://github.com/algolzw/dcls"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/grayscale-image-denoising-on-urban100-sigma15-1","task":"Grayscale Image Denoising","dataset_variant":"urban100 sigma15","rows":3,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"34.11"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/blind-super-resolution-on-urban100-3x","task":"Blind Super-Resolution","dataset_variant":"Urban100 - 3x upscaling","rows":2,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"DCLS","paper":"/paper/deep-constrained-least-squares-for-blind","metrics":{"PSNR":"28.03","SSIM":"0.8444"},"code_links":[{"title":"megvii-research/dcls-sr","url":"https://github.com/megvii-research/dcls-sr"},{"title":"algolzw/dcls","url":"https://github.com/algolzw/dcls"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-urban100-sigma10","task":"Color Image Denoising","dataset_variant":"Urban100 sigma10","rows":2,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Residual Dense Network +","paper":"/paper/residual-dense-network-for-image-restoration","metrics":{"PSNR":"36.75"},"code_links":[{"title":"yulunzhang/RDN","url":"https://github.com/yulunzhang/RDN"},{"title":"QLinhHub/Image-super-resolution","url":"https://github.com/QLinhHub/Image-super-resolution"},{"title":"2023-MindSpore-1/ms-code-216","url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-urban100-sigma30","task":"Color Image Denoising","dataset_variant":"Urban100 sigma30","rows":2,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Restormer-Local","paper":"/paper/revisiting-global-statistics-aggregation-for","metrics":{"PSNR":"33.06"},"code_links":[{"title":"megvii-research/NAFNet","url":"https://github.com/megvii-research/NAFNet"},{"title":"megvii-research/TLC","url":"https://github.com/megvii-research/TLC"},{"title":"setsunil/dsdnet","url":"https://github.com/setsunil/dsdnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/grayscale-image-denoising-on-urban100-sigma70","task":"Grayscale Image Denoising","dataset_variant":"Urban100 sigma70","rows":2,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Residual Dense Network +","paper":"/paper/residual-dense-network-for-image-restoration","metrics":{"PSNR":"25.71"},"code_links":[{"title":"yulunzhang/RDN","url":"https://github.com/yulunzhang/RDN"},{"title":"QLinhHub/Image-super-resolution","url":"https://github.com/QLinhHub/Image-super-resolution"},{"title":"2023-MindSpore-1/ms-code-216","url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-denoising-on-urban100-sigma25","task":"Image Denoising","dataset_variant":"Urban100 sigma25","rows":2,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"MaIR+","paper":"/paper/mair-a-locality-and-continuity-preserving","metrics":{"PSNR":"33.3"},"code_links":[{"title":"XLearning-SCU/2025-CVPR-MaIR","url":"https://github.com/XLearning-SCU/2025-CVPR-MaIR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-urban100-sigma70","task":"Color Image Denoising","dataset_variant":"Urban100 sigma70","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Residual Dense Network +","paper":"/paper/residual-dense-network-for-image-restoration","metrics":{"PSNR":"27.74"},"code_links":[{"title":"yulunzhang/RDN","url":"https://github.com/yulunzhang/RDN"},{"title":"QLinhHub/Image-super-resolution","url":"https://github.com/QLinhHub/Image-super-resolution"},{"title":"2023-MindSpore-1/ms-code-216","url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/compressive-sensing-on-urban100-2x-upscaling","task":"Compressive Sensing","dataset_variant":"Urban100 - 2x upscaling","rows":1,"metrics":["Average PSNR"],"first_row_in_archive_order":{"model":"AMPA-Net","paper":"/paper/ampa-net-optimization-inspired-attention","metrics":{"Average PSNR":"35.86"},"code_links":[{"title":"puallee/AMPA-Net","url":"https://github.com/puallee/AMPA-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/grayscale-image-denoising-on-urban100-sigma10","task":"Grayscale Image Denoising","dataset_variant":"Urban100 sigma10","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Residual Dense Network +","paper":"/paper/residual-dense-network-for-image-restoration","metrics":{"PSNR":"35.45"},"code_links":[{"title":"yulunzhang/RDN","url":"https://github.com/yulunzhang/RDN"},{"title":"QLinhHub/Image-super-resolution","url":"https://github.com/QLinhHub/Image-super-resolution"},{"title":"2023-MindSpore-1/ms-code-216","url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/grayscale-image-denoising-on-urban100-sigma30","task":"Grayscale Image Denoising","dataset_variant":"Urban100 sigma30","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"Residual Dense Network +","paper":"/paper/residual-dense-network-for-image-restoration","metrics":{"PSNR":"30.08"},"code_links":[{"title":"yulunzhang/RDN","url":"https://github.com/yulunzhang/RDN"},{"title":"QLinhHub/Image-super-resolution","url":"https://github.com/QLinhHub/Image-super-resolution"},{"title":"2023-MindSpore-1/ms-code-216","url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-super-resolution-on-urban100-16x","task":"Image Super-Resolution","dataset_variant":"Urban100 - 16x upscaling","rows":1,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"ABPN","paper":"/paper/image-super-resolution-via-attention-based","metrics":{"PSNR":"20.39","SSIM":"0.515"},"code_links":[{"title":"Holmes-Alan/ABPN","url":"https://github.com/Holmes-Alan/ABPN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/progressive-focused-transformer-for-single","title":"Progressive Focused Transformer for Single Image Super-Resolution","date":"2025-03-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":7,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/akdt-adaptive-kernel-dilation-transformer-for","title":"AKDT: Adaptive Kernel Dilation Transformer for Effective Image Denoising","date":"2025-02-26","rows_on_this_dataset":5,"code_links":1,"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":8,"code_links":1,"syntology":null},{"paper":"/paper/auto-encoded-supervision-for-perceptual-image","title":"Auto-Encoded Supervision for Perceptual Image Super-Resolution","date":"2024-11-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":2,"samples_unverified":4,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hierarchical-information-flow-for-generalized","title":"Hierarchical Information Flow for Generalized Efficient Image Restoration","date":"2024-11-27","rows_on_this_dataset":9,"code_links":0,"syntology":null},{"paper":"/paper/ml-craist-multi-scale-low-high-frequency","title":"ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer","date":"2024-08-19","rows_on_this_dataset":6,"code_links":1,"syntology":null},{"paper":"/paper/channel-partitioned-windowed-attention-and","title":"Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution","date":"2024-07-23","rows_on_this_dataset":6,"code_links":0,"syntology":null},{"paper":"/paper/hmanet-hybrid-multi-axis-aggregation-network","title":"HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution","date":"2024-05-08","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/drct-saving-image-super-resolution-away-from","title":"DRCT: Saving Image Super-resolution away from Information Bottleneck","date":"2024-03-31","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/image-processing-gnn-breaking-rigidity-in","title":"Image Processing GNN: Breaking Rigidity in Super-Resolution","date":"2024-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/extracter-efficient-texture-matching-with","title":"EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution","date":"2023-10-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/data-upcycling-knowledge-distillation-for","title":"Data Upcycling Knowledge Distillation for Image Super-Resolution","date":"2023-09-25","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/stimulating-the-diffusion-model-for-image","title":"Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling","date":"2023-07-08","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/a-framework-for-real-time-object-detection","title":"Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy","date":"2023-03-16","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/kbnet-kernel-basis-network-for-image","title":"KBNet: Kernel Basis Network for Image Restoration","date":"2023-03-06","rows_on_this_dataset":6,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/feature-based-adaptive-contrastive","title":"Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution","date":"2022-11-29","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/perception-oriented-single-image-super","title":"Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation","date":"2022-11-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/iitransformer-a-unified-approach-to","title":"iiTransformer: A Unified Approach to Exploiting Local and Non-Local Information for Image Restoration","date":"2022-11-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/swinfir-revisiting-the-swinir-with-fast","title":"SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution","date":"2022-08-24","rows_on_this_dataset":6,"code_links":2,"syntology":null},{"paper":"/paper/activating-more-pixels-in-image-super","title":"Activating More Pixels in Image Super-Resolution Transformer","date":"2022-05-09","rows_on_this_dataset":6,"code_links":2,"syntology":null},{"paper":"/paper/practical-blind-denoising-via-swin-conv-unet","title":"Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis","date":"2022-03-24","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-constrained-least-squares-for-blind","title":"Deep Constrained Least Squares for Blind Image Super-Resolution","date":"2022-02-15","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"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":5,"code_links":3,"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":5,"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/local-texture-estimator-for-implicit","title":"Local Texture Estimator for Implicit Representation Function","date":"2021-11-17","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":0,"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":8,"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/one-to-many-approach-for-improving-super","title":"One-to-many Approach for Improving Super-Resolution","date":"2021-06-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pre-trained-image-processing-transformer","title":"Pre-Trained Image Processing Transformer","date":"2020-12-01","rows_on_this_dataset":2,"code_links":6,"syntology":null},{"paper":"/paper/ampa-net-optimization-inspired-attention","title":"AMPA-Net: Optimization-Inspired Attention Neural Network for Deep Compressed Sensing","date":"2020-10-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unfolding-the-alternating-optimization-for","title":"Unfolding the Alternating Optimization for Blind Super Resolution","date":"2020-10-06","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/single-image-super-resolution-via-a-holistic","title":"Single Image Super-Resolution via a Holistic Attention Network","date":"2020-08-20","rows_on_this_dataset":4,"code_links":2,"syntology":null},{"paper":"/paper/sub-pixel-back-projection-network-for","title":"Sub-Pixel Back-Projection Network For Lightweight Single Image Super-Resolution","date":"2020-08-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-step-reinforcement-learning-for-single","title":"Multi-Step Reinforcement Learning for Single Image Super-Resolution","date":"2020-07-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/progressive-multi-scale-residual-network-for","title":"Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution","date":"2020-07-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/image-super-resolution-with-cross-scale-non","title":"Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining","date":"2020-06-02","rows_on_this_dataset":3,"code_links":3,"syntology":null},{"paper":"/paper/structure-preserving-super-resolution-with","title":"Structure-Preserving Super Resolution with Gradient Guidance","date":"2020-03-29","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/image-super-resolution-via-attention-based","title":"Image Super-Resolution via Attention based Back Projection Networks","date":"2019-10-10","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/lightweight-image-super-resolution-with-1","title":"Lightweight Image Super-Resolution with Information Multi-distillation Network","date":"2019-09-26","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/lcscnet-linear-compressing-based-skip","title":"LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution","date":"2019-09-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/progressive-perception-oriented-network-for","title":"Progressive Perception-Oriented Network for Single Image Super-Resolution","date":"2019-07-24","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/deep-graph-convolutional-image-denoising","title":"Deep Graph-Convolutional Image Denoising","date":"2019-07-19","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/gated-multiple-feedback-network-for-image","title":"Gated Multiple Feedback Network for Image Super-Resolution","date":"2019-07-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/densely-residual-laplacian-super-resolution","title":"Densely Residual Laplacian Super-Resolution","date":"2019-06-28","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hierarchical-back-projection-network-for","title":"Hierarchical Back Projection Network for Image Super-Resolution","date":"2019-06-17","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/second-order-attention-network-for-single","title":"Second-Order Attention Network for Single Image Super-Resolution","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/blind-super-resolution-with-iterative-kernel","title":"Blind Super-Resolution With Iterative Kernel Correction","date":"2019-04-06","rows_on_this_dataset":3,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":2,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-back-projection-networks-for-single","title":"Deep Back-Projection Networks for Single Image Super-resolution","date":"2019-04-04","rows_on_this_dataset":3,"code_links":7,"syntology":null},{"paper":"/paper/feedback-network-for-image-super-resolution","title":"Feedback Network for Image Super-Resolution","date":"2019-03-23","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":2,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/image-super-resolution-by-neural-texture","title":"Image Super-Resolution by Neural Texture Transfer","date":"2019-03-03","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/fast-accurate-and-lightweight-super","title":"Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search","date":"2019-01-22","rows_on_this_dataset":1,"code_links":2,"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/image-super-resolution-via-rl-csc-when","title":"Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding","date":"2018-12-31","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/residual-dense-network-for-image-restoration","title":"Residual Dense Network for Image Restoration","date":"2018-12-25","rows_on_this_dataset":8,"code_links":3,"syntology":null},{"paper":"/paper/lightweight-and-efficient-image-super","title":"Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network","date":"2018-11-30","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/ram-residual-attention-module-for-single","title":"MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution","date":"2018-11-29","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/neural-nearest-neighbors-networks","title":"Neural Nearest Neighbors Networks","date":"2018-10-30","rows_on_this_dataset":3,"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":2,"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/image-super-resolution-using-very-deep","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","date":"2018-07-08","rows_on_this_dataset":1,"code_links":20,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":10,"samples_unverified":12,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/non-local-recurrent-network-for-image","title":"Non-Local Recurrent Network for Image Restoration","date":"2018-06-07","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-level-wavelet-cnn-for-image-restoration","title":"Multi-level Wavelet-CNN for Image Restoration","date":"2018-05-18","rows_on_this_dataset":6,"code_links":5,"syntology":null},{"paper":"/paper/image-super-resolution-via-dual-state","title":"Image Super-Resolution via Dual-State Recurrent Networks","date":"2018-05-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-fully-progressive-approach-to-single-image","title":"A Fully Progressive Approach to Single-Image Super-Resolution","date":"2018-04-09","rows_on_this_dataset":1,"code_links":6,"syntology":null},{"paper":"/paper/fast-and-accurate-single-image-super","title":"Fast and Accurate Single Image Super-Resolution via Information Distillation Network","date":"2018-03-26","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fast-accurate-and-lightweight-super-1","title":"Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network","date":"2018-03-23","rows_on_this_dataset":1,"code_links":3,"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/deep-back-projection-networks-for-super","title":"Deep Back-Projection Networks For Super-Resolution","date":"2018-03-07","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":6,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/residual-dense-network-for-image-super","title":"Residual Dense Network for Image Super-Resolution","date":"2018-02-24","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":7,"samples_unverified":17,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/sesr-single-image-super-resolution-with","title":"SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks","date":"2018-01-31","rows_on_this_dataset":1,"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/ffdnet-toward-a-fast-and-flexible-solution","title":"FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising","date":"2017-10-11","rows_on_this_dataset":1,"code_links":8,"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/memnet-a-persistent-memory-network-for-image","title":"MemNet: A Persistent Memory Network for Image Restoration","date":"2017-08-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/enhanced-deep-residual-networks-for-single","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","date":"2017-07-10","rows_on_this_dataset":1,"code_links":45,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-laplacian-pyramid-networks-for-fast-and","title":"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution","date":"2017-04-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/enhancenet-single-image-super-resolution","title":"EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis","date":"2016-12-23","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/beyond-deep-residual-learning-for-image","title":"Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification","date":"2016-11-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/beyond-a-gaussian-denoiser-residual-learning","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","date":"2016-08-13","rows_on_this_dataset":6,"code_links":22,"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/deeply-recursive-convolutional-network-for","title":"Deeply-Recursive Convolutional Network for Image Super-Resolution","date":"2015-11-14","rows_on_this_dataset":1,"code_links":1,"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/accurate-image-super-resolution-using-very","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","date":"2015-11-14","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/trainable-nonlinear-reaction-diffusion-a","title":"Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration","date":"2015-08-12","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/image-super-resolution-using-deep","title":"Image Super-Resolution Using Deep Convolutional Networks","date":"2014-12-31","rows_on_this_dataset":1,"code_links":60,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":27,"samples_ran":7,"samples_unverified":20,"pointer_only_for_licence":7,"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":296,"samples_ran":119,"samples_unverified":177,"pointer_only_for_licence":41,"papers_with_no_sample_that_ran":8,"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."}