{"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/automatic-photo-adjustment-using-deep-neural","title":"Automatic Photo Adjustment Using Deep Neural Networks","arxiv_id":"1412.7725","date":"2014-12-24","proceeding":null,"authors":["Zhicheng Yan","Hao Zhang","Baoyuan Wang","Sylvain Paris","Yizhou Yu"],"abstract":"Photo retouching enables photographers to invoke dramatic visual impressions\nby artistically enhancing their photos through stylistic color and tone\nadjustments. However, it is also a time-consuming and challenging task that\nrequires advanced skills beyond the abilities of casual photographers. Using an\nautomated algorithm is an appealing alternative to manual work but such an\nalgorithm faces many hurdles. Many photographic styles rely on subtle\nadjustments that depend on the image content and even its semantics. Further,\nthese adjustments are often spatially varying. Because of these\ncharacteristics, existing automatic algorithms are still limited and cover only\na subset of these challenges. Recently, deep machine learning has shown unique\nabilities to address hard problems that resisted machine algorithms for long.\nThis motivated us to explore the use of deep learning in the context of photo\nediting. In this paper, we explain how to formulate the automatic photo\nadjustment problem in a way suitable for this approach. We also introduce an\nimage descriptor that accounts for the local semantics of an image. Our\nexperiments demonstrate that our deep learning formulation applied using these\ndescriptors successfully capture sophisticated photographic styles. In\nparticular and unlike previous techniques, it can model local adjustments that\ndepend on the image semantics. We show on several examples that this yields\nresults that are qualitatively and quantitatively better than previous work.","url_abs":"http://arxiv.org/abs/1412.7725v2","url_pdf":"http://arxiv.org/pdf/1412.7725v2.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":"automatic-photo-adjustment-using-deep-neural","repo_url":"https://github.com/stephenyan1984/dl-image-enhance","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"photo-retouching","task_name":"Photo Retouching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}