{"url":"/dataset/dnd","name":"DND","full_name":"Darmstadt Noise Dataset","description_markdown":"Benchmarking Denoising Algorithms with Real Photographs\r\n\r\nThis dataset consists of 50 pairs of noisy and (nearly) noise-free images captured with four consumer cameras. Since the images are of very high-resolution, the providers extract 20 crops of size 512 × 512 from each image, thus yielding a total of 1000 patches.","description_withheld":null,"homepage":"https://noise.visinf.tu-darmstadt.de/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Denoising","url":"/task/denoising","datasets_with_task":"/datasets/task/denoising"},{"name":"Image Denoising","url":"/task/image-denoising","datasets_with_task":"/datasets/task/image-denoising"}],"languages":[],"variants":["DND"],"data_loaders":[],"num_papers_in_archive":25,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-denoising-on-dnd","task":"Image Denoising","dataset_variant":"DND","rows":16,"metrics":["PSNR (sRGB)","SSIM (sRGB)"],"first_row_in_archive_order":{"model":"DualDn","paper":"/paper/dualdn-dual-domain-denoising-via","metrics":{"PSNR (sRGB)":"40.594","SSIM (sRGB)":"0.966"},"code_links":[{"title":"OpenImagingLab/DualDn","url":"https://github.com/OpenImagingLab/DualDn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/denoising-on-dnd-1","task":"Denoising","dataset_variant":"DND","rows":1,"metrics":["Average PSNR","SSIM (sRGB)"],"first_row_in_archive_order":{"model":"DRANet","paper":"/paper/dual-residual-attention-network-for-image","metrics":{"Average PSNR":"39.64","SSIM (sRGB)":"0.952"},"code_links":[{"title":"WenCongWu/DRANet","url":"https://github.com/WenCongWu/DRANet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dualdn-dual-domain-denoising-via","title":"DualDn: Dual-domain Denoising via Differentiable ISP","date":"2024-09-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/single-stage-adaptive-multi-attention-network","title":"Single Stage Adaptive Multi-Attention Network for Image Restoration","date":"2024-04-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dual-residual-attention-network-for-image","title":"Dual Residual Attention Network for Image Denoising","date":"2023-05-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-to-generate-realistic-noisy-images-2","title":"Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training","date":"2022-04-06","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":14,"samples_unverified":4,"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":1,"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":1,"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/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":1,"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/multi-stage-progressive-image-restoration","title":"Multi-Stage Progressive Image Restoration","date":"2021-02-04","rows_on_this_dataset":1,"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/nbnet-noise-basis-learning-for-image","title":"NBNet: Noise Basis Learning for Image Denoising with Subspace Projection","date":"2020-12-30","rows_on_this_dataset":1,"code_links":9,"syntology":null},{"paper":"/paper/dual-adversarial-network-toward-real-world","title":"Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation","date":"2020-07-12","rows_on_this_dataset":1,"code_links":2,"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/cycleisp-real-image-restoration-via-improved","title":"CycleISP: Real Image Restoration via Improved Data Synthesis","date":"2020-03-17","rows_on_this_dataset":1,"code_links":8,"syntology":null},{"paper":"/paper/learning-enriched-features-for-real-image","title":"Learning Enriched Features for Real Image Restoration and Enhancement","date":"2020-03-15","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":3,"samples_unverified":17,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/transfer-learning-from-synthetic-to-real-2","title":"Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization","date":"2020-02-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spatial-adaptive-network-for-single-image","title":"Spatial-Adaptive Network for Single Image Denoising","date":"2020-01-28","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/variational-denoising-network-toward-blind","title":"Variational Denoising Network: Toward Blind Noise Modeling and Removal","date":"2019-08-29","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/real-image-denoising-with-feature-attention","title":"Real Image Denoising with Feature Attention","date":"2019-04-16","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/toward-convolutional-blind-denoising-of-real","title":"Toward Convolutional Blind Denoising of Real Photographs","date":"2018-07-12","rows_on_this_dataset":1,"code_links":3,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":8,"samples_harvested":122,"samples_ran":73,"samples_unverified":49,"pointer_only_for_licence":35,"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."}