Conventional photography can only provide a two-dimensional image of the scene, whereas emerging imaging modalities such as light field enable the representation of higher dimensional visual information by capturing light rays from different directions. Light fields provide immersive experiences, a sense of presence in the scene, and can enhance different vision tasks. Hence, research into light field processing methods has become increasingly popular. It does, however, come at the cost of higher data volume and computational complexity. With the growing deployment of machine-learning and deep architectures in image processing applications, a paradigm shift toward learning-based approaches has also been observed in the design of light field processing methods. Various learning-based approaches are developed to process the high volume of light field data efficiently for different vision tasks while improving performance. Taking into account the diversity of light field vision tasks and the deployed learning-based frameworks, it is necessary to survey the scattered learning-based works in the domain to gain insight into the current trends and challenges. This paper aims to review the existing learning-based solutions for light field imaging and to summarize the most promising frameworks. Moreover, evaluation methods and available light field datasets are highlighted. Lastly, the review concludes with a brief outlook for future research directions.
Mahmoudpour, S, Pagliari, C & Schelkens, P 2024, 'Learning-based Light field Imaging: An Overview', EURASIP Journal on Image and Video Processing, vol. 2024, no. 1, 12. https://doi.org/10.1186/s13640-024-00628-1
Mahmoudpour, S., Pagliari, C., & Schelkens, P. (2024). Learning-based Light field Imaging: An Overview. EURASIP Journal on Image and Video Processing, 2024(1), Article 12. https://doi.org/10.1186/s13640-024-00628-1
@article{c13a6a8d03a34facb203188427bcd311,
title = "Learning-based Light field Imaging: An Overview",
abstract = "Conventional photography can only provide a two-dimensional image of the scene, whereas emerging imaging modalities such as light field enable the representation of higher dimensional visual information by capturing light rays from different directions. Light fields provide immersive experiences, a sense of presence in the scene, and can enhance different vision tasks. Hence, research into light field processing methods has become increasingly popular. It does, however, come at the cost of higher data volume and computational complexity. With the growing deployment of machine-learning and deep architectures in image processing applications, a paradigm shift toward learning-based approaches has also been observed in the design of light field processing methods. Various learning-based approaches are developed to process the high volume of light field data efficiently for different vision tasks while improving performance. Taking into account the diversity of light field vision tasks and the deployed learning-based frameworks, it is necessary to survey the scattered learning-based works in the domain to gain insight into the current trends and challenges. This paper aims to review the existing learning-based solutions for light field imaging and to summarize the most promising frameworks. Moreover, evaluation methods and available light field datasets are highlighted. Lastly, the review concludes with a brief outlook for future research directions.",
author = "Saeed Mahmoudpour and Carla Pagliari and Peter Schelkens",
note = "Funding Information: The research was funded by Research Foundation\u2014Flanders (FWO) (G0B3521N). Publisher Copyright: {\textcopyright} The Author(s) 2024.",
year = "2024",
doi = "10.1186/s13640-024-00628-1",
language = "English",
volume = "2024",
journal = "EURASIP Journal on Image and Video Processing",
issn = "1687-5176",
publisher = "Springer Publishing Company",
number = "1",
}