{"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/high-resolution-image-inpainting-using-multi","title":"High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis","arxiv_id":"1611.09969","date":"2016-11-30","proceeding":"CVPR 2017 7","authors":["Chao Yang","Xin Lu","Zhe Lin","Eli Shechtman","Oliver Wang","Hao Li"],"abstract":"Recent advances in deep learning have shown exciting promise in filling large\nholes in natural images with semantically plausible and context aware details,\nimpacting fundamental image manipulation tasks such as object removal. While\nthese learning-based methods are significantly more effective in capturing\nhigh-level features than prior techniques, they can only handle very\nlow-resolution inputs due to memory limitations and difficulty in training.\nEven for slightly larger images, the inpainted regions would appear blurry and\nunpleasant boundaries become visible. We propose a multi-scale neural patch\nsynthesis approach based on joint optimization of image content and texture\nconstraints, which not only preserves contextual structures but also produces\nhigh-frequency details by matching and adapting patches with the most similar\nmid-layer feature correlations of a deep classification network. We evaluate\nour method on the ImageNet and Paris Streetview datasets and achieved\nstate-of-the-art inpainting accuracy. We show our approach produces sharper and\nmore coherent results than prior methods, especially for high-resolution\nimages.","url_abs":"http://arxiv.org/abs/1611.09969v2","url_pdf":"http://arxiv.org/pdf/1611.09969v2.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":"high-resolution-image-inpainting-using-multi","repo_url":"https://github.com/leehomyc/Faster-High-Res-Neural-Inpainting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":"image-manipulation","task_name":"Image Manipulation"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"3d-convolution","method_name":"3D Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.09969","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}