{"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/zero-shot-structure-preserving-diffusion","title":"Zero-Shot Structure-Preserving Diffusion Model for High Dynamic Range Tone Mapping","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Ruoxi Zhu","Shusong Xu","Peiye Liu","Sicheng Li","Yanheng Lu","Dimin Niu","Zihao Liu","Zihao Meng","Zhiyong Li","Xinhua Chen","Yibo Fan"],"abstract":"    Tone mapping techniques aiming to convert high dynamic range (HDR) images to high-quality low dynamic range (LDR) images for display play a more crucial role in real-world vision systems with the increasing application of HDR images. However obtaining paired HDR and high-quality LDR images is difficult posing a challenge to deep learning based tone mapping methods. To overcome this challenge we propose a novel zero-shot tone mapping framework that utilizes shared structure knowledge allowing us to transfer a pre-trained mapping model from the LDR domain to HDR fields without paired training data. Our approach involves decomposing both the LDR and HDR images into two components: structural information and tonal information. To preserve the original image's structure we modify the reverse sampling process of a diffusion model and explicitly incorporate the structure information into the intermediate results. Additionally for improved image details we introduce a dual-control network architecture that enables different types of conditional inputs to control different scales of the output. Experimental results demonstrate the effectiveness of our approach surpassing previous state-of-the-art methods both qualitatively and quantitatively. Moreover our model exhibits versatility and can be applied to other low-level vision tasks without retraining. The code is available at https://github.com/ZSDM-HDR/Zero-Shot-Diffusion-HDR.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Zhu_Zero-Shot_Structure-Preserving_Diffusion_Model_for_High_Dynamic_Range_Tone_Mapping_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Zhu_Zero-Shot_Structure-Preserving_Diffusion_Model_for_High_Dynamic_Range_Tone_Mapping_CVPR_2024_paper.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":"zero-shot-structure-preserving-diffusion","repo_url":"https://github.com/zsdm-hdr/zero-shot-diffusion-hdr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"tone-mapping","task_name":"Tone Mapping"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}