期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2018
卷号：15
期号：1
DOI：10.1177/1729881417748446
语种：English
出版社：SAGE Publications
摘要：With the development of consumer light field cameras, the light field imaging has become an extensively used method for capturing the three-dimensional appearance of a scene. The depth estimation often requires a dense sampled light field in the angular domain or a high resolution in the spatial domain. However, there is an inherent trade-off between the angular and spatial resolutions of the light field. Recently, some studies for super-resolving the trade-off light field have been introduced. Rather than the conventional approaches that optimize the depth maps, these approaches focus on maximizing the quality of the super-resolved light field. In this article, we investigate how the depth estimation can benefit from these super-resolution methods. Specifically, we compare the qualities of the estimated depth using (a) the original sparse sampled light fields and the reconstructed dense sampled light fields, and (b) the original low-resolution light fields and the high-resolution light fields. Experiment results evaluate the enhanced depth maps using different super-resolution approaches.
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