{"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-photorealistic-image","title":"High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network","arxiv_id":"2105.09188","date":"2021-05-19","proceeding":"CVPR 2021 1","authors":["Jie Liang","Hui Zeng","Lei Zhang"],"abstract":"Existing image-to-image translation (I2IT) methods are either constrained to low-resolution images or long inference time due to their heavy computational burden on the convolution of high-resolution feature maps. In this paper, we focus on speeding-up the high-resolution photorealistic I2IT tasks based on closed-form Laplacian pyramid decomposition and reconstruction. Specifically, we reveal that the attribute transformations, such as illumination and color manipulation, relate more to the low-frequency component, while the content details can be adaptively refined on high-frequency components. We consequently propose a Laplacian Pyramid Translation Network (LPTN) to simultaneously perform these two tasks, where we design a lightweight network for translating the low-frequency component with reduced resolution and a progressive masking strategy to efficiently refine the high-frequency ones. Our model avoids most of the heavy computation consumed by processing high-resolution feature maps and faithfully preserves the image details. Extensive experimental results on various tasks demonstrate that the proposed method can translate 4K images in real-time using one normal GPU while achieving comparable transformation performance against existing methods. Datasets and codes are available: https://github.com/csjliang/LPTN.","url_abs":"https://arxiv.org/abs/2105.09188v1","url_pdf":"https://arxiv.org/pdf/2105.09188v1.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-photorealistic-image","repo_url":"https://github.com/csjliang/LPTN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"4k","task_name":"4k"},{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"color-manipulation","task_name":"Color Manipulation"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"photo-retouching","task_name":"Photo Retouching"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/photo-retouching-on-mit-adobe-5k","task":"Photo Retouching","dataset":"MIT-Adobe 5k","model":"LPTN (L=3)","rank_in_archive_order":5,"of":5,"metrics":{"PSNR":"22.02","SSIM":"0.879"},"uses_additional_data":false},{"leaderboard":"/sota/photo-retouching-on-mit-adobe-5k-1080p","task":"Photo Retouching","dataset":"MIT-Adobe 5k (1080p)","model":"LPTN (L=3)","rank_in_archive_order":1,"of":2,"metrics":{"PSNR":"22.09","SSIM":"0.883"},"uses_additional_data":false},{"leaderboard":"/sota/photo-retouching-on-mit-adobe-5k-1080p","task":"Photo Retouching","dataset":"MIT-Adobe 5k (1080p)","model":"DPE","rank_in_archive_order":2,"of":2,"metrics":{"PSNR":"21.94","SSIM":"0.885"},"uses_additional_data":false},{"leaderboard":"/sota/photo-retouching-on-mit-adobe-5k-480p","task":"Photo Retouching","dataset":"MIT-Adobe 5k (480p)","model":"LPTN (L=3)","rank_in_archive_order":1,"of":2,"metrics":{"PSNR":"22.12","SSIM":"0.878"},"uses_additional_data":false},{"leaderboard":"/sota/photo-retouching-on-mit-adobe-5k-480p","task":"Photo Retouching","dataset":"MIT-Adobe 5k (480p)","model":"DPE","rank_in_archive_order":2,"of":2,"metrics":{"PSNR":"21.99","SSIM":"0.875"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2105.09188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.09188"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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