{"url":"/dataset/synthetic-rain-datasets","name":"Synthetic Rain Datasets","full_name":null,"description_markdown":"The Synthetic Rain Datasets consists of 13,712 clean-rain image pairs gathered from multiple datasets (Rain14000, Rain1800, Rain800, Rain12). With a single trained model, evaluation could be performed on various test sets, including Rain100H, Rain100L, Test100, Test2800, and Test1200.\r\n\r\nPSNR and SSIM are computed on Y-channel in YCbCr color space.","description_withheld":null,"homepage":"","introduced_date":"2020-03-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/multi-scale-progressive-fusion-network-for","title":"Multi-Scale Progressive Fusion Network for Single Image Deraining","first_author":"Kui Jiang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Single Image Deraining","url":"/task/single-image-deraining","datasets_with_task":"/datasets/task/single-image-deraining"},{"name":"Unified Image Restoration","url":"/task/unified-image-restoration","datasets_with_task":"/datasets/task/unified-image-restoration"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["Synthetic Rain Datasets","Rain100H","Rain100L","Test100","Test1200","Test2800"],"data_loaders":[],"num_papers_in_archive":102,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/single-image-deraining-on-rain100h","task":"Single Image Deraining","dataset_variant":"Rain100H","rows":19,"metrics":["PSNR","SSIM","FID","LPIPS"],"first_row_in_archive_order":{"model":"GOUB (Mean-ODE)","paper":"/paper/image-restoration-through-generalized","metrics":{"PSNR":"34.56","SSIM":"0.9414"},"code_links":[{"title":"Hammour-steak/GOUB","url":"https://github.com/Hammour-steak/GOUB"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/single-image-deraining-on-rain100l","task":"Single Image Deraining","dataset_variant":"Rain100L","rows":19,"metrics":["PSNR","SSIM","FID","LPIPS"],"first_row_in_archive_order":{"model":"IPT","paper":"/paper/pre-trained-image-processing-transformer","metrics":{"PSNR":"41.62","SSIM":"0.988"},"code_links":[{"title":"huawei-noah/Pretrained-IPT","url":"https://github.com/huawei-noah/Pretrained-IPT"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/IPT"},{"title":"Mind23-2/MindCode-3","url":"https://github.com/Mind23-2/MindCode-3/tree/main/IPT"},{"title":"2023-MindSpore-1/ms-code-214","url":"https://github.com/2023-MindSpore-1/ms-code-214/tree/main/IPT"},{"title":"dongyan007/Pretrained-IPT-main-master","url":"https://github.com/dongyan007/Pretrained-IPT-main-master"},{"title":"yangyucheng000/IPT-3","url":"https://github.com/yangyucheng000/IPT-3"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/single-image-deraining-on-test1200","task":"Single Image Deraining","dataset_variant":"Test1200","rows":14,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"CAPTNet","paper":"/paper/prompt-based-all-in-one-image-restoration","metrics":{"PSNR":"34.77","SSIM":"0.937"},"code_links":[{"title":"Tombs98/CAPTNet","url":"https://github.com/Tombs98/CAPTNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/single-image-deraining-on-test100","task":"Single Image Deraining","dataset_variant":"Test100","rows":12,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"Restormer","paper":"/paper/restormer-efficient-transformer-for-high","metrics":{"PSNR":"32.00","SSIM":"0.923"},"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/single-image-deraining-on-test2800","task":"Single Image Deraining","dataset_variant":"Test2800","rows":12,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"KBNet","paper":"/paper/kbnet-kernel-basis-network-for-image","metrics":{"PSNR":"34.19","SSIM":"0.944"},"code_links":[{"title":"zhangyi-3/kbnet","url":"https://github.com/zhangyi-3/kbnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unified-image-restoration-on-rain100l","task":"Unified Image Restoration","dataset_variant":"Rain100L","rows":1,"metrics":["Average PSNR (dB)"],"first_row_in_archive_order":{"model":"DA-RCOT","paper":"/paper/degradation-aware-residual-conditioned","metrics":{"Average PSNR (dB)":"38.36"},"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/forward-only-diffusion-probabilistic-models","title":"Forward-only Diffusion Probabilistic Models","date":"2025-05-22","rows_on_this_dataset":2,"code_links":1,"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/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/image-restoration-through-generalized","title":"Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge","date":"2023-12-16","rows_on_this_dataset":1,"code_links":1,"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/controlling-vision-language-models-for","title":"Controlling Vision-Language Models for Multi-Task Image Restoration","date":"2023-10-02","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":8,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":2,"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/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":4,"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/kbnet-kernel-basis-network-for-image","title":"KBNet: Kernel Basis Network for Image Restoration","date":"2023-03-06","rows_on_this_dataset":2,"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/mixed-hierarchy-network-for-image-restoration","title":"Mixed Hierarchy Network for Image Restoration","date":"2023-02-19","rows_on_this_dataset":4,"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":2,"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/maxim-multi-axis-mlp-for-image-processing","title":"MAXIM: Multi-Axis MLP for Image Processing","date":"2022-01-09","rows_on_this_dataset":5,"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/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/hinet-half-instance-normalization-network-for","title":"HINet: Half Instance Normalization Network for Image Restoration","date":"2021-05-13","rows_on_this_dataset":5,"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":5,"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/pre-trained-image-processing-transformer","title":"Pre-Trained Image Processing Transformer","date":"2020-12-01","rows_on_this_dataset":1,"code_links":6,"syntology":null},{"paper":"/paper/mara-net-single-image-deraining-network-with","title":"MCW-Net: Single Image Deraining with Multi-level Connections and Wide Regional Non-local Blocks","date":"2020-09-29","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/multi-scale-progressive-fusion-network-for","title":"Multi-Scale Progressive Fusion Network for Single Image Deraining","date":"2020-03-24","rows_on_this_dataset":5,"code_links":3,"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/uncertainty-guided-multi-scale-residual-1","title":"Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining","date":"2019-06-12","rows_on_this_dataset":5,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":4,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/progressive-image-deraining-networks-a-better","title":"Progressive Image Deraining Networks: A Better and Simpler Baseline","date":"2019-01-26","rows_on_this_dataset":3,"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":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/semi-supervised-cnn-for-single-image-rain","title":"Semi-supervised Transfer Learning for Image Rain Removal","date":"2018-07-29","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/recurrent-squeeze-and-excitation-context","title":"Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining","date":"2018-07-16","rows_on_this_dataset":5,"code_links":0,"syntology":null},{"paper":"/paper/density-aware-single-image-de-raining-using-a","title":"Density-aware Single Image De-raining using a Multi-stream Dense Network","date":"2018-02-21","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/clearing-the-skies-a-deep-network","title":"Clearing the Skies: A deep network architecture for single-image rain removal","date":"2016-09-07","rows_on_this_dataset":5,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":12,"samples_harvested":143,"samples_ran":87,"samples_unverified":56,"pointer_only_for_licence":48,"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."}