{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/renoir-a-dataset-for-real-low-light-image","title":"RENOIR - A Dataset for Real Low-Light Image Noise Reduction","arxiv_id":"1409.8230","date":"2014-09-29","proceeding":null,"authors":["Josue Anaya","Adrian Barbu"],"abstract":"Image denoising algorithms are evaluated using images corrupted by artificial\nnoise, which may lead to incorrect conclusions about their performances on real\nnoise. In this paper we introduce a dataset of color images corrupted by\nnatural noise due to low-light conditions, together with spatially and\nintensity-aligned low noise images of the same scenes. We also introduce a\nmethod for estimating the true noise level in our images, since even the low\nnoise images contain small amounts of noise. We evaluate the accuracy of our\nnoise estimation method on real and artificial noise, and investigate the\nPoisson-Gaussian noise model. Finally, we use our dataset to evaluate six\ndenoising algorithms: Active Random Field, BM3D, Bilevel-MRF, Multi-Layer\nPerceptron, and two versions of NL-means. We show that while the Multi-Layer\nPerceptron, Bilevel-MRF, and NL-means with soft threshold outperform BM3D on\ngray images with synthetic noise, they lag behind on our dataset.","url_abs":"http://arxiv.org/abs/1409.8230v9","url_pdf":"http://arxiv.org/pdf/1409.8230v9.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"renoir-a-dataset-for-real-low-light-image","repo_url":"https://github.com/Aftaab99/DenoisingAutoencoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"color-image-denoising","task_name":"Color Image Denoising"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"noise-estimation","task_name":"Noise Estimation"}],"methods":[],"datasets_introduced":[{"slug":"renoir","name":"RENOIR","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/color-image-denoising-on-renoir","task":"Color Image Denoising","dataset":"RENOIR","model":"BM3D","rank_in_archive_order":1,"of":2,"metrics":{"Average PSNR":"36.355"},"uses_additional_data":false},{"leaderboard":"/sota/color-image-denoising-on-renoir","task":"Color Image Denoising","dataset":"RENOIR","model":"ARF","rank_in_archive_order":2,"of":2,"metrics":{"Average PSNR":"33.755"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1409.8230","atlas_url":"https://app.syntology.ai/?focus=1409.8230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1409.8230"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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