{"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/connecting-image-denoising-and-high-level","title":"Connecting Image Denoising and High-Level Vision Tasks via Deep Learning","arxiv_id":"1809.01826","date":"2018-09-06","proceeding":null,"authors":["Ding Liu","Bihan Wen","Jianbo Jiao","Xian-Ming Liu","Zhangyang Wang","Thomas S. Huang"],"abstract":"Image denoising and high-level vision tasks are usually handled independently\nin the conventional practice of computer vision, and their connection is\nfragile. In this paper, we cope with the two jointly and explore the mutual\ninfluence between them with the focus on two questions, namely (1) how image\ndenoising can help improving high-level vision tasks, and (2) how the semantic\ninformation from high-level vision tasks can be used to guide image denoising.\nFirst for image denoising we propose a convolutional neural network in which\nconvolutions are conducted in various spatial resolutions via downsampling and\nupsampling operations in order to fuse and exploit contextual information on\ndifferent scales. Second we propose a deep neural network solution that\ncascades two modules for image denoising and various high-level tasks,\nrespectively, and use the joint loss for updating only the denoising network\nvia back-propagation. We experimentally show that on one hand, the proposed\ndenoiser has the generality to overcome the performance degradation of\ndifferent high-level vision tasks. On the other hand, with the guidance of\nhigh-level vision information, the denoising network produces more visually\nappealing results. Extensive experiments demonstrate the benefit of exploiting\nimage semantics simultaneously for image denoising and high-level vision tasks\nvia deep learning. The code is available online:\nhttps://github.com/Ding-Liu/DeepDenoising","url_abs":"http://arxiv.org/abs/1809.01826v1","url_pdf":"http://arxiv.org/pdf/1809.01826v1.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":"connecting-image-denoising-and-high-level","repo_url":"https://github.com/Ding-Liu/DeepDenoising","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.01826","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}