{"url":"/task/single-image-deraining","name":"Single Image Deraining","slug":"single-image-deraining","description_markdown":null,"categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":94,"papers_with_code":59,"benchmarks":9,"benchmark_tables_in_archive":9,"benchmark_tables_shown":9,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":6,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/single-image-deraining-on-rain100h","slug":"single-image-deraining-on-rain100h","dataset":"Rain100H","dataset_url":"/dataset/synthetic-rain-datasets","rows_in_archive":19,"metrics":["PSNR","SSIM","FID","LPIPS"],"first_row_in_archive_order":{"model":"GOUB (Mean-ODE)","paper_title":"Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge","paper_url":"/paper/image-restoration-through-generalized","paper_date":"2023-12-16","arxiv_id":"2312.10299","code_links":[{"title":"Hammour-steak/GOUB","url":"https://github.com/Hammour-steak/GOUB"}],"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/single-image-deraining-on-rain100l","slug":"single-image-deraining-on-rain100l","dataset":"Rain100L","dataset_url":"/dataset/synthetic-rain-datasets","rows_in_archive":19,"metrics":["PSNR","SSIM","FID","LPIPS"],"first_row_in_archive_order":{"model":"IPT","paper_title":"Pre-Trained Image Processing Transformer","paper_url":"/paper/pre-trained-image-processing-transformer","paper_date":"2020-12-01","arxiv_id":"2012.00364","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"}],"syntology":null}},{"leaderboard":"/sota/single-image-deraining-on-test1200","slug":"single-image-deraining-on-test1200","dataset":"Test1200","dataset_url":"/dataset/synthetic-rain-datasets","rows_in_archive":14,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"CAPTNet","paper_title":"Prompt-based Ingredient-Oriented All-in-One Image Restoration","paper_url":"/paper/prompt-based-all-in-one-image-restoration","paper_date":"2023-09-06","arxiv_id":"2309.03063","code_links":[{"title":"Tombs98/CAPTNet","url":"https://github.com/Tombs98/CAPTNet"}],"syntology":{"n":12,"n_ran":11,"n_unverified":1,"n_pointer_only":12}}},{"leaderboard":"/sota/single-image-deraining-on-test100","slug":"single-image-deraining-on-test100","dataset":"Test100","dataset_url":"/dataset/synthetic-rain-datasets","rows_in_archive":12,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"Restormer","paper_title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","paper_url":"/paper/restormer-efficient-transformer-for-high","paper_date":"2021-11-18","arxiv_id":"2111.09881","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"}],"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":4}}},{"leaderboard":"/sota/single-image-deraining-on-test2800","slug":"single-image-deraining-on-test2800","dataset":"Test2800","dataset_url":"/dataset/synthetic-rain-datasets","rows_in_archive":12,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"KBNet","paper_title":"KBNet: Kernel Basis Network for Image Restoration","paper_url":"/paper/kbnet-kernel-basis-network-for-image","paper_date":"2023-03-06","arxiv_id":"2303.02881","code_links":[{"title":"zhangyi-3/kbnet","url":"https://github.com/zhangyi-3/kbnet"}],"syntology":{"n":9,"n_ran":6,"n_unverified":3,"n_pointer_only":0}}},{"leaderboard":"/sota/single-image-deraining-on-raincityscapes","slug":"single-image-deraining-on-raincityscapes","dataset":"RainCityscapes","dataset_url":"/dataset/raincityscapes","rows_in_archive":6,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"MCW-Net","paper_title":"MCW-Net: Single Image Deraining with Multi-level Connections and Wide Regional Non-local Blocks","paper_url":"/paper/mara-net-single-image-deraining-network-with","paper_date":"2020-09-29","arxiv_id":"2009.13990","code_links":[{"title":"yechanp/MCW-Net","url":"https://github.com/yechanp/MCW-Net"}],"syntology":null}},{"leaderboard":"/sota/single-image-deraining-on-raindrop","slug":"single-image-deraining-on-raindrop","dataset":"Raindrop","dataset_url":"/dataset/raindrop","rows_in_archive":3,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"TransWeather","paper_title":"TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions","paper_url":"/paper/transweather-transformer-based-restoration-of","paper_date":"2021-11-29","arxiv_id":"2111.14813","code_links":[{"title":"jeya-maria-jose/TransWeather","url":"https://github.com/jeya-maria-jose/TransWeather"}],"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/single-image-deraining-on-rain12","slug":"single-image-deraining-on-rain12","dataset":"Rain12","dataset_url":null,"rows_in_archive":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"PReNet","paper_title":"Progressive Image Deraining Networks: A Better and Simpler Baseline","paper_url":"/paper/progressive-image-deraining-networks-a-better","paper_date":"2019-01-26","arxiv_id":"1901.09221","code_links":[{"title":"csdwren/PReNet","url":"https://github.com/csdwren/PReNet"},{"title":"simonsLiang/PReNet_paddle","url":"https://github.com/simonsLiang/PReNet_paddle"},{"title":"GuoQuanhao/PRNet-Paddle","url":"https://github.com/GuoQuanhao/PRNet-Paddle"},{"title":"Leozhibin/PReNet-master","url":"https://github.com/Leozhibin/PReNet-master"}],"syntology":{"n":9,"n_ran":1,"n_unverified":8,"n_pointer_only":0}}},{"leaderboard":"/sota/single-image-deraining-on-rain1400","slug":"single-image-deraining-on-rain1400","dataset":"Rain1400","dataset_url":null,"rows_in_archive":1,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"PReNetr","paper_title":"Progressive Image Deraining Networks: A Better and Simpler Baseline","paper_url":"/paper/progressive-image-deraining-networks-a-better","paper_date":"2019-01-26","arxiv_id":"1901.09221","code_links":[{"title":"csdwren/PReNet","url":"https://github.com/csdwren/PReNet"},{"title":"simonsLiang/PReNet_paddle","url":"https://github.com/simonsLiang/PReNet_paddle"},{"title":"GuoQuanhao/PRNet-Paddle","url":"https://github.com/GuoQuanhao/PRNet-Paddle"},{"title":"Leozhibin/PReNet-master","url":"https://github.com/Leozhibin/PReNet-master"}],"syntology":{"n":9,"n_ran":1,"n_unverified":8,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/raindrop","name":"Raindrop","full_name":"","num_papers_in_archive":120},{"url":"/dataset/synthetic-rain-datasets","name":"Synthetic Rain Datasets","full_name":"","num_papers_in_archive":102},{"url":"/dataset/raincityscapes","name":"RainCityscapes","full_name":"RainCityscapes","num_papers_in_archive":29},{"url":"/dataset/real-rain-dataset","name":"Real Rain Dataset","full_name":"","num_papers_in_archive":11},{"url":"/dataset/awmm-100k","name":"AWMM-100k","full_name":"","num_papers_in_archive":1},{"url":"/dataset/hri","name":"HRI","full_name":"High-resolution Rainy Image","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/rain-removal","name":"Rain Removal"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":59,"tagged_in_all":94,"items":[{"url":"/paper/restormer-efficient-transformer-for-high","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","date":"2021-11-18","arxiv_id":"2111.09881","repositories_listed":13,"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":4}},{"url":"/paper/multi-stage-progressive-image-restoration","title":"Multi-Stage Progressive Image Restoration","date":"2021-02-04","arxiv_id":"2102.02808","repositories_listed":8,"syntology":{"n":26,"n_ran":18,"n_unverified":8,"n_pointer_only":25}},{"url":"/paper/pre-trained-image-processing-transformer","title":"Pre-Trained Image Processing Transformer","date":"2020-12-01","arxiv_id":"2012.00364","repositories_listed":6,"syntology":null},{"url":"/paper/progressive-image-deraining-networks-a-better","title":"Progressive Image Deraining Networks: A Better and Simpler Baseline","date":"2019-01-26","arxiv_id":"1901.09221","repositories_listed":4,"syntology":{"n":9,"n_ran":1,"n_unverified":8,"n_pointer_only":0}},{"url":"/paper/maxim-multi-axis-mlp-for-image-processing","title":"MAXIM: Multi-Axis MLP for Image Processing","date":"2022-01-09","arxiv_id":"2201.02973","repositories_listed":3,"syntology":{"n":46,"n_ran":27,"n_unverified":19,"n_pointer_only":0}},{"url":"/paper/sdnet-mutil-branch-for-single-image-deraining","title":"SDNet: mutil-branch for single image deraining using swin","date":"2021-05-31","arxiv_id":"2105.15077","repositories_listed":3,"syntology":null},{"url":"/paper/multi-scale-progressive-fusion-network-for","title":"Multi-Scale Progressive Fusion Network for Single Image Deraining","date":"2020-03-24","arxiv_id":"2003.10985","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/nasnet-a-neuron-attention-stage-by-stage-net","title":"NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining","date":"2019-12-06","arxiv_id":"1912.03151","repositories_listed":3,"syntology":null},{"url":"/paper/hinet-half-instance-normalization-network-for","title":"HINet: Half Instance Normalization Network for Image Restoration","date":"2021-05-13","arxiv_id":"2105.06086","repositories_listed":2,"syntology":null},{"url":"/paper/efficientderain-learning-pixel-wise-dilation","title":"EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining","date":"2020-09-19","arxiv_id":"2009.09238","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/spatial-attentive-single-image-deraining-with","title":"Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset","date":"2019-04-02","arxiv_id":"1904.01538","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/clearing-the-skies-a-deep-network","title":"Clearing the Skies: A deep network architecture for single-image rain removal","date":"2016-09-07","arxiv_id":"1609.02087","repositories_listed":2,"syntology":null},{"url":"/paper/forward-only-diffusion-probabilistic-models","title":"Forward-only Diffusion Probabilistic Models","date":"2025-05-22","arxiv_id":"2505.16733","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-optimal-attention-and-local-model","title":"Iterative Optimal Attention and Local Model for Single Image Rain Streak Removal","date":"2025-03-20","arxiv_id":"2503.16165","repositories_listed":1,"syntology":null},{"url":"/paper/unified-image-restoration-and-enhancement","title":"Unified Image Restoration and Enhancement: Degradation Calibrated Cycle Reconstruction Diffusion Model","date":"2024-12-19","arxiv_id":"2412.14630","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-transformer-mamba-network-for-single","title":"A Hybrid Transformer-Mamba Network for Single Image Deraining","date":"2024-08-31","arxiv_id":"2409.00410","repositories_listed":1,"syntology":null},{"url":"/paper/explore-internal-and-external-similarity-for","title":"Explore Internal and External Similarity for Single Image Deraining with Graph Neural Networks","date":"2024-06-02","arxiv_id":"2406.00721","repositories_listed":1,"syntology":{"n":17,"n_ran":7,"n_unverified":10,"n_pointer_only":17}},{"url":"/paper/diving-deep-into-regions-exploiting-regional","title":"Exploiting Regional Information Transformer for Single Image Deraining","date":"2024-02-25","arxiv_id":"2402.16033","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-potential-of-channel","title":"Exploring the potential of channel interactions for image restoration","date":"2023-12-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/image-restoration-through-generalized","title":"Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge","date":"2023-12-16","arxiv_id":"2312.10299","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/resfusion-prior-residual-noise-embedded","title":"Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise","date":"2023-11-25","arxiv_id":"2311.14900","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":5}},{"url":"/paper/controlling-vision-language-models-for","title":"Controlling Vision-Language Models for Multi-Task Image Restoration","date":"2023-10-02","arxiv_id":"2310.01018","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/latent-degradation-representation-constraint","title":"Latent Degradation Representation Constraint for Single Image Deraining","date":"2023-09-09","arxiv_id":"2309.04780","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-based-all-in-one-image-restoration","title":"Prompt-based Ingredient-Oriented All-in-One Image Restoration","date":"2023-09-06","arxiv_id":"2309.03063","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_unverified":1,"n_pointer_only":12}},{"url":"/paper/from-sky-to-the-ground-a-large-scale","title":"From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal","date":"2023-08-07","arxiv_id":"2308.03867","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_unverified":1,"n_pointer_only":14}},{"url":"/paper/prores-exploring-degradation-aware-visual","title":"ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration","date":"2023-06-23","arxiv_id":"2306.13653","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/a-two-stage-real-image-deraining-method-for","title":"A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG$^{\\textbf{2}}$+ Track 3","date":"2023-05-13","arxiv_id":"2305.07979","repositories_listed":1,"syntology":null},{"url":"/paper/a-mountain-shaped-single-stage-network-for","title":"A Mountain-Shaped Single-Stage Network for Accurate Image Restoration","date":"2023-05-09","arxiv_id":"2305.05146","repositories_listed":1,"syntology":null},{"url":"/paper/selective-frequency-network-for-image","title":"Selective Frequency Network for Image Restoration","date":"2023-04-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/kbnet-kernel-basis-network-for-image","title":"KBNet: Kernel Basis Network for Image Restoration","date":"2023-03-06","arxiv_id":"2303.02881","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_unverified":3,"n_pointer_only":0}}],"syntology_records":15,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}