{"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/deep-class-aware-denoising","title":"Deep Class Aware Denoising","arxiv_id":"1701.01698","date":"2017-01-06","proceeding":null,"authors":["Tal Remez","Or Litany","Raja Giryes","Alex M. Bronstein"],"abstract":"The increasing demand for high image quality in mobile devices brings forth\nthe need for better computational enhancement techniques, and image denoising\nin particular. At the same time, the images captured by these devices can be\ncategorized into a small set of semantic classes. However simple, this\nobservation has not been exploited in image denoising until now. In this paper,\nwe demonstrate how the reconstruction quality improves when a denoiser is aware\nof the type of content in the image. To this end, we first propose a new fully\nconvolutional deep neural network architecture which is simple yet powerful as\nit achieves state-of-the-art performance even without being class-aware. We\nfurther show that a significant boost in performance of up to $0.4$ dB PSNR can\nbe achieved by making our network class-aware, namely, by fine-tuning it for\nimages belonging to a specific semantic class. Relying on the hugely successful\nexisting image classifiers, this research advocates for using a class-aware\napproach in all image enhancement tasks.","url_abs":"http://arxiv.org/abs/1701.01698v2","url_pdf":"http://arxiv.org/pdf/1701.01698v2.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":"deep-class-aware-denoising","repo_url":"https://github.com/TalRemez/deep_class_aware_denoising","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}