{"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/kunet-imaging-knowledge-inspired-single-hdr","title":"KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction","arxiv_id":null,"date":"2022-07-23","proceeding":"The 31st International Joint Conference On Artificial Intelligence 2022 7","authors":["Hu Wang","Mao Ye","Xiatian Zhu","Shuai Li","Ce Zhu","Xue Li"],"abstract":"Recently, with the rise of high dynamic range (HDR) display devices, there is a great demand to transfer traditional low dynamic range (LDR) images into HDR versions. The key to success is how to solve the many-to-many mapping problem. However, the existing approaches either do not consider constraining solution space or just simply imitate the inverse camera imaging pipeline in stages, without directly formulating the HDR image generation process. In this work, we address this problem by integrating LDR-to-HDR imaging knowledge into an UNet architecture, dubbed as Knowledge-inspired UNet (KUNet). The conversion from LDR-to-HDR image is mathematically formulated, and can be conceptually divided into recovering missing details, adjusting imaging parameters and reducing imaging noise. Accordingly , we develop a basic knowledge-inspired block (KIB) including three subnetworks corresponding to the three procedures in this HDR imaging process. The KIB blocks are cascaded in the similar way to the UNet to construct HDR image with rich global information. In addition, we also propose a knowledge inspired jump-connect structure to fit a dynamic range gap between HDR and LDR images. Experimental results demonstrate that the proposed KUNet achieves superior performance compared with the state-of-the-art methods. The code, dataset and appendix materials are available at https://github.com/wanghu178/KUNet.git.","url_abs":"https://openresearch.surrey.ac.uk/esploro/outputs/conferencePaper/KUNet-Imaging-Knowledge-Inspired-Single-HDR-Image/99652966202346","url_pdf":"https://openresearch.surrey.ac.uk/esploro/outputs/conferencePaper/KUNet-Imaging-Knowledge-Inspired-Single-HDR-Image/99652966202346#file-0","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":"kunet-imaging-knowledge-inspired-single-hdr","repo_url":"https://github.com/wanghu178/KUNet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"inverse-tone-mapping-1","task_name":"Inverse-Tone-Mapping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/inverse-tone-mapping-on-msu-hdr-video","task":"Inverse-Tone-Mapping","dataset":"MSU HDR Video Reconstruction Benchmark","model":"KUNet","rank_in_archive_order":8,"of":9,"metrics":{"HDR-PSNR":"32.5082","HDR-SSIM":"0.9864","HDR-VQM":"0.2126"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}