{"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/when-awgn-based-denoiser-meets-real-noises","title":"When AWGN-based Denoiser Meets Real Noises","arxiv_id":"1904.03485","date":"2019-04-06","proceeding":null,"authors":["Yuqian Zhou","Jianbo Jiao","Haibin Huang","Yang Wang","Jue Wang","Honghui Shi","Thomas Huang"],"abstract":"Discriminative learning-based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. First, we train a deep model that consists of a noise estimator and a denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then investigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trained model to real noises. Extensive experiments demonstrate the effectiveness and generalization of the proposed approach. Notably, our method achieves state-of-the-art performance on real sRGB images in the DND benchmark among models trained with synthetic noises. Codes are available at https://github.com/yzhouas/PD-Denoising-pytorch.","url_abs":"https://arxiv.org/abs/1904.03485v2","url_pdf":"https://arxiv.org/pdf/1904.03485v2.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":"when-awgn-based-denoiser-meets-real-noises","repo_url":"https://github.com/yzhouas/PD-Denoising-pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"when-awgn-based-denoiser-meets-real-noises","repo_url":"https://github.com/kritiksoman/GIMP-ML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/denoising-on-darmstadt-noise-dataset","task":"Denoising","dataset":"Darmstadt Noise Dataset","model":"Pixel-shuffling Downsampling","rank_in_archive_order":2,"of":10,"metrics":{"PSNR":"38.4"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.03485","atlas_url":"https://app.syntology.ai/?focus=1904.03485","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}