{"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-chain-hdri-reconstructing-a-high-dynamic","title":"Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image","arxiv_id":"1801.06277","date":"2018-01-19","proceeding":null,"authors":["Siyeong Lee","Gwon Hwan An","Suk-Ju Kang"],"abstract":"In this paper, we propose a novel deep neural network model that reconstructs\na high dynamic range (HDR) image from a single low dynamic range (LDR) image.\nThe proposed model is based on a convolutional neural network composed of\ndilated convolutional layers, and infers LDR images with various exposures and\nillumination from a single LDR image of the same scene. Then, the final HDR\nimage can be formed by merging these inference results. It is relatively easy\nfor the proposed method to find the mapping between the LDR and an HDR with a\ndifferent bit depth because of the chaining structure inferring the\nrelationship between the LDR images with brighter (or darker) exposures from a\ngiven LDR image. The method not only extends the range, but also has the\nadvantage of restoring the light information of the actual physical world. For\nthe HDR images obtained by the proposed method, the HDR-VDP2 Q score, which is\nthe most popular evaluation metric for HDR images, was 56.36 for a display with\na 1920$\\times$1200 resolution, which is an improvement of 6 compared with the\nscores of conventional algorithms. In addition, when comparing the peak\nsignal-to-noise ratio values for tone mapped HDR images generated by the\nproposed and conventional algorithms, the average value obtained by the\nproposed algorithm is 30.86 dB, which is 10 dB higher than those obtained by\nthe conventional algorithms.","url_abs":"http://arxiv.org/abs/1801.06277v1","url_pdf":"http://arxiv.org/pdf/1801.06277v1.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":[],"tasks":[{"task_slug":"inverse-tone-mapping","task_name":"inverse tone mapping"}],"methods":[],"datasets_introduced":[{"slug":"vds-dataset","name":"VDS dataset: Multi exposure stack-based inverse tone mapping","full_name":"VDS dataset: Multi exposure stack-based inverse tone mapping"}],"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 Chain HDRI","rank_in_archive_order":5,"of":9,"metrics":{"HDR-VDP-2":"56.36","Kim and Kautz TMO-PSNR":"24.54","Reinhard'TMO-PSNR":"30.86"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.06277","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}