Modern High Dynamic Range Imaging at the Time of Deep Learning
| dc.contributor.author | Banterle, Francesco | en_US |
| dc.contributor.author | Artusi, Alessandro | en_US |
| dc.contributor.editor | Serrano, Ana | en_US |
| dc.contributor.editor | Slusallek, Philipp | en_US |
| dc.date.accessioned | 2023-05-03T05:58:05Z | |
| dc.date.available | 2023-05-03T05:58:05Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | In this tutorial, we introduce how the High Dynamic Range (HDR) imaging field has evolved in this new era where machine learning approaches have become dominant. The main reason of this success is that the use of machine learning and deep learning have automatized many tedious tasks achieving high-quality results overperforming classic methods. After an introduction on classic HDR imaging and its open problem, we will summarize the main approaches for: merging of multiple exposures, single image reconstructions or inverse tone mapping, tone mapping, and display visualization. Finally, we will highlights the still open problems in this machine learning era, and possible direction on how to solve them. | en_US |
| dc.description.sectionheaders | Tutorials | |
| dc.description.seriesinformation | Eurographics 2023 - Tutorials | |
| dc.identifier.doi | 10.2312/egt.20231033 | |
| dc.identifier.isbn | 978-3-03868-212-7 | |
| dc.identifier.issn | 1017-4656 | |
| dc.identifier.pages | 15-19 | |
| dc.identifier.pages | 5 pages | |
| dc.identifier.uri | https://doi.org/10.2312/egt.20231033 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/egt20231033 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Modern High Dynamic Range Imaging at the Time of Deep Learning | en_US |