{"url":"/dataset/mcmaster","name":"McMaster","full_name":null,"description_markdown":"The **McMaster** dataset is a dataset for color demosaicing, which contains 18 cropped images of size 500×500.\r\n\r\nSource: [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://arxiv.org/abs/1710.04026)\r\nImage Source: [https://www4.comp.polyu.edu.hk/~cslzhang/paper/LMMSEdemosaicing.pdf](https://www4.comp.polyu.edu.hk/~cslzhang/paper/LMMSEdemosaicing.pdf)","description_withheld":null,"homepage":"https://www4.comp.polyu.edu.hk/~cslzhang/CDM_Dataset.htm","introduced_date":"2011-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Color demosaicking by local directional interpolation and nonlocal adaptive thresholding","first_author":null,"url":"https://doi.org/10.1117/1.3600632"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Color Image Denoising","url":"/task/color-image-denoising","datasets_with_task":"/datasets/task/color-image-denoising"},{"name":"Joint Demosaicing and Denoising","url":"/task/joint-demosaicing-and-denoising","datasets_with_task":"/datasets/task/joint-demosaicing-and-denoising"}],"languages":[],"variants":["McMaster sigma15","McMaster sigma25","McMaster sigma35","McMaster sigma50","McMaster sigma75","McMaster"],"data_loaders":[],"num_papers_in_archive":101,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/color-image-denoising-on-mcmaster-sigma50","task":"Color Image Denoising","dataset_variant":"McMaster sigma50","rows":7,"metrics":["PSNR","LPIPS","SSIM"],"first_row_in_archive_order":{"model":"AKDT","paper":"/paper/akdt-adaptive-kernel-dilation-transformer-for","metrics":{"PSNR":"30.95"},"code_links":[{"title":"albrateanu/AKDT","url":"https://github.com/albrateanu/AKDT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-mcmaster-sigma15","task":"Color Image Denoising","dataset_variant":"McMaster sigma15","rows":4,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"AKDT","paper":"/paper/akdt-adaptive-kernel-dilation-transformer-for","metrics":{"PSNR":"36.71"},"code_links":[{"title":"albrateanu/AKDT","url":"https://github.com/albrateanu/AKDT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-mcmaster-sigma25","task":"Color Image Denoising","dataset_variant":"McMaster sigma25","rows":4,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"AKDT","paper":"/paper/akdt-adaptive-kernel-dilation-transformer-for","metrics":{"PSNR":"34.21"},"code_links":[{"title":"albrateanu/AKDT","url":"https://github.com/albrateanu/AKDT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-mcmaster-sigma35","task":"Color Image Denoising","dataset_variant":"McMaster sigma35","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"FFDNet","paper":"/paper/ffdnet-toward-a-fast-and-flexible-solution","metrics":{"PSNR":"30.81"},"code_links":[{"title":"cszn/FFDNet","url":"https://github.com/cszn/FFDNet"},{"title":"mq0829/DL-CACTI","url":"https://github.com/mq0829/DL-CACTI"},{"title":"Aoi-hosizora/FFDNet_pytorch","url":"https://github.com/Aoi-hosizora/FFDNet_pytorch"},{"title":"7568/ffdnet-pytorch","url":"https://github.com/7568/ffdnet-pytorch"},{"title":"deshanyang/liver-dir-qa","url":"https://github.com/deshanyang/liver-dir-qa"},{"title":"SamirMitha/Denoising","url":"https://github.com/SamirMitha/Denoising"},{"title":"LucasElbert/FFDNet","url":"https://github.com/LucasElbert/FFDNet"},{"title":"deshanyang/abdominal-dir-qa","url":"https://github.com/deshanyang/abdominal-dir-qa"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/color-image-denoising-on-mcmaster-sigma75","task":"Color Image Denoising","dataset_variant":"McMaster sigma75","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"FFDNet","paper":"/paper/ffdnet-toward-a-fast-and-flexible-solution","metrics":{"PSNR":"27.33"},"code_links":[{"title":"cszn/FFDNet","url":"https://github.com/cszn/FFDNet"},{"title":"mq0829/DL-CACTI","url":"https://github.com/mq0829/DL-CACTI"},{"title":"Aoi-hosizora/FFDNet_pytorch","url":"https://github.com/Aoi-hosizora/FFDNet_pytorch"},{"title":"7568/ffdnet-pytorch","url":"https://github.com/7568/ffdnet-pytorch"},{"title":"deshanyang/liver-dir-qa","url":"https://github.com/deshanyang/liver-dir-qa"},{"title":"SamirMitha/Denoising","url":"https://github.com/SamirMitha/Denoising"},{"title":"LucasElbert/FFDNet","url":"https://github.com/LucasElbert/FFDNet"},{"title":"deshanyang/abdominal-dir-qa","url":"https://github.com/deshanyang/abdominal-dir-qa"}]},"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":3,"code_links":1,"syntology":null},{"paper":"/paper/hierarchical-information-flow-for-generalized","title":"Hierarchical Information Flow for Generalized Efficient Image Restoration","date":"2024-11-27","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/stimulating-the-diffusion-model-for-image","title":"Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling","date":"2023-07-08","rows_on_this_dataset":5,"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/ffdnet-toward-a-fast-and-flexible-solution","title":"FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising","date":"2017-10-11","rows_on_this_dataset":5,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"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":2,"samples_harvested":16,"samples_ran":6,"samples_unverified":10,"pointer_only_for_licence":0,"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."}