{"url":"/sota/single-image-deraining-on-rain100h","task":{"name":"Single Image Deraining","url":"/task/single-image-deraining","note":null},"dataset":{"name":"Rain100H","url":"/dataset/synthetic-rain-datasets"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":null,"description_from":null,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["PSNR","SSIM","FID","LPIPS"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"PSNR":"higher","SSIM":"higher","FID":"lower","LPIPS":null}},"counts":{"rows":19,"rows_with_code":18,"rows_with_paper_page":19,"rows_dated":19,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"GOUB (Mean-ODE)","metrics":{"PSNR":"34.56","SSIM":"0.9414"},"uses_additional_data":false,"paper_date":"2023-12-16","paper":"/paper/image-restoration-through-generalized","paper_url":"https://arxiv.org/abs/2312.10299v2","paper_title":"Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge","code":"https://github.com/Hammour-steak/GOUB","n_code_links":1,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"DA-CLIP","metrics":{"PSNR":"33.91","SSIM":"0.926"},"uses_additional_data":false,"paper_date":"2023-10-02","paper":"/paper/controlling-vision-language-models-for","paper_url":"https://arxiv.org/abs/2310.01018v2","paper_title":"Controlling Vision-Language Models for Multi-Task Image Restoration","code":"https://github.com/algolzw/daclip-uir","n_code_links":1,"syntology":{"n_ran":8,"n_unverified":1,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"Fod w/ NMC","metrics":{"FID":"15.64","LPIPS":"0.041","PSNR":"33.63","SSIM":"0.941"},"uses_additional_data":false,"paper_date":"2025-05-22","paper":"/paper/forward-only-diffusion-probabilistic-models","paper_url":"https://arxiv.org/abs/2505.16733v1","paper_title":"Forward-only Diffusion Probabilistic Models","code":"https://github.com/Algolzw/FoD","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"FoD","metrics":{"FID":"14.10","LPIPS":"0.038","PSNR":"32.56","SSIM":"0.925"},"uses_additional_data":false,"paper_date":"2025-05-22","paper":"/paper/forward-only-diffusion-probabilistic-models","paper_url":"https://arxiv.org/abs/2505.16733v1","paper_title":"Forward-only Diffusion Probabilistic Models","code":"https://github.com/Algolzw/FoD","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"IR-SDE","metrics":{"FID":"18.64","LPIPS":"0.047","PSNR":"31.65","SSIM":"0.9041"},"uses_additional_data":false,"paper_date":"2023-01-27","paper":"/paper/image-restoration-with-mean-reverting","paper_url":"https://arxiv.org/abs/2301.11699v3","paper_title":"Image Restoration with Mean-Reverting Stochastic Differential Equations","code":"https://github.com/algolzw/image-restoration-sde","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":6,"model":"Restormer","metrics":{"PSNR":"31.46","SSIM":"0.904"},"uses_additional_data":false,"paper_date":"2021-11-18","paper":"/paper/restormer-efficient-transformer-for-high","paper_url":"https://arxiv.org/abs/2111.09881v2","paper_title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","code":"https://github.com/swz30/restormer","n_code_links":13,"syntology":{"n_ran":4,"n_unverified":0,"n_samples":4,"n_pointer_only_licence":4}},{"rank_in_archive_order":7,"model":"MCW-Net","metrics":{"PSNR":"30.70","SSIM":"0.922"},"uses_additional_data":false,"paper_date":"2020-09-29","paper":"/paper/mara-net-single-image-deraining-network-with","paper_url":"https://arxiv.org/abs/2009.13990v4","paper_title":"MCW-Net: Single Image Deraining with Multi-level Connections and Wide Regional Non-local Blocks","code":"https://github.com/yechanp/MCW-Net","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"HINet","metrics":{"PSNR":"30.65","SSIM":"0.894"},"uses_additional_data":false,"paper_date":"2021-05-13","paper":"/paper/hinet-half-instance-normalization-network-for","paper_url":"https://arxiv.org/abs/2105.06086v2","paper_title":"HINet: Half Instance Normalization Network for Image Restoration","code":"https://github.com/megvii-model/HINet","n_code_links":2,"syntology":null},{"rank_in_archive_order":9,"model":"M3SNet","metrics":{"PSNR":"30.64","SSIM":"0.892"},"uses_additional_data":false,"paper_date":"2023-05-09","paper":"/paper/a-mountain-shaped-single-stage-network-for","paper_url":"https://arxiv.org/abs/2305.05146v1","paper_title":"A Mountain-Shaped Single-Stage Network for Accurate Image Restoration","code":"https://github.com/Tombs98/M3SNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":10,"model":"MPRNet","metrics":{"PSNR":"30.41","SSIM":"0.89"},"uses_additional_data":false,"paper_date":"2021-02-04","paper":"/paper/multi-stage-progressive-image-restoration","paper_url":"https://arxiv.org/abs/2102.02808v2","paper_title":"Multi-Stage Progressive Image Restoration","code":"https://github.com/swz30/restormer","n_code_links":8,"syntology":{"n_ran":18,"n_unverified":8,"n_samples":26,"n_pointer_only_licence":25}},{"rank_in_archive_order":11,"model":"MHNet","metrics":{"PSNR":"30.34"},"uses_additional_data":false,"paper_date":"2023-02-19","paper":"/paper/mixed-hierarchy-network-for-image-restoration","paper_url":"https://arxiv.org/abs/2302.09554v4","paper_title":"Mixed Hierarchy Network for Image Restoration","code":"https://github.com/tombs98/mhnet","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"PReNet","metrics":{"PSNR":"29.46","SSIM":"0.899"},"uses_additional_data":false,"paper_date":"2019-01-26","paper":"/paper/progressive-image-deraining-networks-a-better","paper_url":"https://arxiv.org/abs/1901.09221v3","paper_title":"Progressive Image Deraining Networks: A Better and Simpler Baseline","code":"https://github.com/csdwren/PReNet","n_code_links":4,"syntology":{"n_ran":1,"n_unverified":8,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":13,"model":"MSPFN","metrics":{"PSNR":"28.66","SSIM":"0.86"},"uses_additional_data":false,"paper_date":"2020-03-24","paper":"/paper/multi-scale-progressive-fusion-network-for","paper_url":"https://arxiv.org/abs/2003.10985v2","paper_title":"Multi-Scale Progressive Fusion Network for Single Image Deraining","code":"https://github.com/kuijiang94/mspfn","n_code_links":3,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":1}},{"rank_in_archive_order":14,"model":"RESCAN","metrics":{"PSNR":"26.36","SSIM":"0.786"},"uses_additional_data":false,"paper_date":"2018-07-16","paper":"/paper/recurrent-squeeze-and-excitation-context","paper_url":"http://arxiv.org/abs/1807.05698v2","paper_title":"Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":15,"model":"SEMI","metrics":{"PSNR":"16.56","SSIM":"0.486"},"uses_additional_data":false,"paper_date":"2018-07-29","paper":"/paper/semi-supervised-cnn-for-single-image-rain","paper_url":"http://arxiv.org/abs/1807.11078v2","paper_title":"Semi-supervised Transfer Learning for Image Rain Removal","code":"https://github.com/wwzjer/Semi-supervised-IRR","n_code_links":1,"syntology":null},{"rank_in_archive_order":16,"model":"MAXIM","metrics":{"SSIM":"0.903"},"uses_additional_data":false,"paper_date":"2022-01-09","paper":"/paper/maxim-multi-axis-mlp-for-image-processing","paper_url":"https://arxiv.org/abs/2201.02973v2","paper_title":"MAXIM: Multi-Axis MLP for Image Processing","code":"https://github.com/google-research/maxim","n_code_links":3,"syntology":{"n_ran":27,"n_unverified":19,"n_samples":46,"n_pointer_only_licence":0}},{"rank_in_archive_order":17,"model":"UMRL","metrics":{"SSIM":"0.832"},"uses_additional_data":false,"paper_date":"2019-06-12","paper":"/paper/uncertainty-guided-multi-scale-residual-1","paper_url":"https://arxiv.org/abs/1906.11129v1","paper_title":"Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining","code":"https://github.com/rajeevyasarla/UMRL--using-Cycle-Spinning","n_code_links":1,"syntology":{"n_ran":4,"n_unverified":13,"n_samples":17,"n_pointer_only_licence":0}},{"rank_in_archive_order":18,"model":"DerainNet","metrics":{"SSIM":"0.592"},"uses_additional_data":false,"paper_date":"2016-09-07","paper":"/paper/clearing-the-skies-a-deep-network","paper_url":"http://arxiv.org/abs/1609.02087v2","paper_title":"Clearing the Skies: A deep network architecture for single-image rain removal","code":"https://github.com/jinnovation/rainy-image-dataset","n_code_links":2,"syntology":null},{"rank_in_archive_order":19,"model":"DIDMDN","metrics":{"SSIM":"0.524"},"uses_additional_data":false,"paper_date":"2018-02-21","paper":"/paper/density-aware-single-image-de-raining-using-a","paper_url":"http://arxiv.org/abs/1802.07412v1","paper_title":"Density-aware Single Image De-raining using a Multi-stream Dense Network","code":"https://github.com/hezhangsprinter/DID-MDN","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":9,"rows_with_any_sample_ran":8,"distinct_papers_with_graph_line":9,"distinct_papers_with_any_sample_ran":8,"samples_over_distinct_papers":{"n_ran":64,"n_unverified":52,"n_samples":116,"n_pointer_only_licence":30,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":64,"n_unverified":52,"n_samples":116,"n_pointer_only_licence":30,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}