{"url":"/dataset/sidd","name":"SIDD","full_name":"Smartphone Image Denoising Dataset","description_markdown":"SIDD is an image denoising dataset containing 30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras. Ground truth images are provided along with the noisy images.\r\n\r\nSource: [A High-Quality Denoising Dataset for Smartphone Cameras](/paper/a-high-quality-denoising-dataset-for)","description_withheld":null,"homepage":"https://www.eecs.yorku.ca/~kamel/sidd/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/a-high-quality-denoising-dataset-for","title":"A High-Quality Denoising Dataset for Smartphone Cameras","first_author":"Abdelrahman Abdelhamed","url":null},"license":{"name":"MIT","url":"https://www.eecs.yorku.ca/~kamel/sidd/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Denoising","url":"/task/denoising","datasets_with_task":"/datasets/task/denoising"},{"name":"Image Restoration","url":"/task/image-restoration","datasets_with_task":"/datasets/task/image-restoration"},{"name":"Image Denoising","url":"/task/image-denoising","datasets_with_task":"/datasets/task/image-denoising"},{"name":"Noise Estimation","url":"/task/noise-estimation","datasets_with_task":"/datasets/task/noise-estimation"}],"languages":[],"variants":["SIDD"],"data_loaders":[],"num_papers_in_archive":245,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-denoising-on-sidd","task":"Image Denoising","dataset_variant":"SIDD","rows":22,"metrics":["PSNR (sRGB)","SSIM (sRGB)","Average PSNR"],"first_row_in_archive_order":{"model":"CGNet","paper":"/paper/cascadedgaze-efficiency-in-global-context","metrics":{"PSNR (sRGB)":"40.39","SSIM (sRGB)":"0.964"},"code_links":[{"title":"Ascend-Research/CascadedGaze","url":"https://github.com/Ascend-Research/CascadedGaze"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/noise-estimation-on-sidd","task":"Noise Estimation","dataset_variant":"SIDD","rows":5,"metrics":["PSNR Gap","Average KL Divergence"],"first_row_in_archive_order":{"model":"PNGAN","paper":"/paper/learning-to-generate-realistic-noisy-images-2","metrics":{"Average KL Divergence":"0.153","PSNR Gap":"0.84"},"code_links":[{"title":"caiyuanhao1998/PNGAN","url":"https://github.com/caiyuanhao1998/PNGAN"},{"title":"GarrickZ2/Image-Denoising","url":"https://github.com/GarrickZ2/Image-Denoising"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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":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/cascadedgaze-efficiency-in-global-context","title":"CascadedGaze: Efficiency in Global Context Extraction for Image Restoration","date":"2024-01-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":10,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/kbnet-kernel-basis-network-for-image","title":"KBNet: Kernel Basis Network for Image Restoration","date":"2023-03-06","rows_on_this_dataset":1,"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/simple-baselines-for-image-restoration","title":"Simple Baselines for Image Restoration","date":"2022-04-10","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":30,"samples_ran":23,"samples_unverified":7,"pointer_only_for_licence":17,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":2,"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; 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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":2,"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/grdngrouped-residual-dense-network-for-real","title":"GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling","date":"2019-05-27","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/unprocessing-images-for-learned-raw-denoising","title":"Unprocessing Images for Learned Raw Denoising","date":"2018-11-27","rows_on_this_dataset":1,"code_links":4,"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":2,"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":11,"samples_harvested":172,"samples_ran":112,"samples_unverified":60,"pointer_only_for_licence":52,"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."}