{"url":"/sota/image-denoising-on-nam","task":{"name":"Image Denoising","url":"/task/image-denoising","note":null},"dataset":{"name":"Nam","url":"/dataset/nam"},"category":"Computer Vision","categories":["Computer Vision","Medical"],"category_note":null,"description":"**Image Denoising** is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Wide Inference Network for Image Denoising via\r\nLearning Pixel-distribution Prior](https://arxiv.org/pdf/1707.05414v5.pdf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["PSNR","SSIM"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"PSNR":"higher","SSIM":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"PNGAN","metrics":{"PSNR":"40.78","SSIM":"0.986"},"uses_additional_data":false,"paper_date":"2022-04-06","paper":"/paper/learning-to-generate-realistic-noisy-images-2","paper_url":"https://arxiv.org/abs/2204.02844v2","paper_title":"Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training","code":"https://github.com/caiyuanhao1998/PNGAN","n_code_links":2,"syntology":{"n_ran":14,"n_unverified":4,"n_samples":18,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":14,"n_unverified":4,"n_samples":18,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":14,"n_unverified":4,"n_samples":18,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}