132231 20241220120715.0 doi 10.1111/exsy.13559 sideral 137483 ART-2025-137483 eng Vélez Bedoya, Jairo Iván Exploring deep learning techniques for illuminance estimation 2025 In recent years, deep learning techniques had a revolutionary impact on several domains, including computer vision and image processing. This research paper focuses on exploring deep learning methods to achieve precise illuminance estimation, which holds significant importance in applications such as augmented reality, virtual reality, and photography. However, accurately estimating illuminance in complex scenes continues to pose challenges due to the intricate interplay between light sources, objects, and surfaces. The results of extensive experimentation demonstrate the immense potential of deep learning techniques in illuminance estimation. These techniques exhibit promising accuracy and robustness, enabling them to handle diverse scenarios effectively. The valuable insights derived from this study can serve as a guiding framework for future research endeavours and contribute to the development of efficient and precise methodologies for illuminance estimation across a wide range of practical applications. info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion González Bedia, Manuel Universidad de Zaragoza (orcid)0000-0002-8263-2444 Castillo Ossa, Luis Fernando Arango-López, Jeferson Díaz-Arancibia, Jaime 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 42, 1 (2025), e13559 [14 pp.] Expert syst. EXPERT SYSTEMS 0266-4720 3849536 http://zaguan.unizar.es/record/132231/files/texto_completo.pdf Versión publicada 2404315 http://zaguan.unizar.es/record/132231/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:132231 articulos driver 2024-12-20-12:05:29 ARTICLE