{"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/deep-recursive-hdri-inverse-tone-mapping","title":"Deep Recursive HDRI: Inverse Tone Mapping using Generative Adversarial Networks","arxiv_id":null,"date":"2018-09-01","proceeding":"ECCV 2018 9","authors":["Siyeong Lee","Gwon Hwan An","Suk-Ju Kang"],"abstract":"High dynamic range images contain luminance information of the physical world and provide more realistic experience than conventional low dynamic range images. Because most images have a low dynamic range, recovering the lost dynamic range from a single low dynamic range image is still prevalent. We propose a novel method for restoring the lost dynamic range from a single low dynamic range image through a deep neural network. The proposed method is the first framework to create high dynamic range images based on the estimated multi-exposure stack using the conditional generative adversarial network structure. In this architecture, we train the network by setting an objective function that is a combination of L1 loss and generative adversarial network loss. In addition, this architecture has a simplified structure than the existing networks. In the experimental results, the proposed network generated a multi-exposure stack consisting of realistic images with varying exposure values while avoiding artifacts on public benchmarks, compared with the existing methods. In addition, both the multi-exposure stacks and high dynamic range images estimated by the proposed method are significantly similar to the ground truth than other state-of-the-art algorithms.","url_abs":"http://openaccess.thecvf.com/content_ECCV_2018/html/Siyeong_Lee_Deep_Recursive_HDRI_ECCV_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ECCV_2018/papers/Siyeong_Lee_Deep_Recursive_HDRI_ECCV_2018_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":"deep-recursive-hdri-inverse-tone-mapping","repo_url":"https://github.com/Siyeong-Lee/Deep_Recursive_HDRI","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"inverse-tone-mapping-1","task_name":"Inverse-Tone-Mapping"},{"task_slug":"tone-mapping","task_name":"Tone Mapping"},{"task_slug":"inverse-tone-mapping","task_name":"inverse tone mapping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/inverse-tone-mapping-on-vds-dataset","task":"inverse tone mapping","dataset":"VDS dataset: Multi exposure stack-based inverse tone mapping","model":"Deep Recursive HDRI","rank_in_archive_order":3,"of":9,"metrics":{"HDR-VDP-2":"57.28","HDR-VDP-3":"8.48","Kim and Kautz TMO-PSNR":"28.02","PU21-PSNR":"25.88","PU21-SSIM":"0.8874","Reinhard'TMO-PSNR":"32.94"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}