Journal
SIGNAL PROCESSING-IMAGE COMMUNICATION
Volume 106, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.image.2022.116721
Keywords
Light field; Computational photography; Axial resolution; Sampling analysis; Digital refocusing
Categories
Funding
- National Natural Science Foundation of China [61871319, 62031023]
- Natural Science Basic Research Plan of Shaanxi Province, China [2019JM-221]
- China Scholarship Council (CSC) [201808610055]
- EU [694122]
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This paper presents a model for axial refocusing precision in light field imaging and demonstrates through experiments that computationally extending the light field baseline can improve the axial refocusing precision.
In light field imaging, axial refocusing precision corresponds to the minimum distance in the axial direction between two distinguishable refocusing planes. The refocusing precision can be essential for applications like light field microscopy. In this paper, we introduce a refocusing precision model based on a geometrical analysis of the flow of rays within the virtual camera. The model establishes the relationship between the feature separability of refocusing and different camera settings. As extending numerical aperture (NA) in classical imaging, the baseline extension of light field also gives more accurate refocusing results. To test the axial refocus precision, we conduct experiments with 1st generation Lytro camera as well as a Blender light field simulation. The results is basically consistent with our prediction. Then, we show that computationally extending the light field baseline increases the axial refocusing precision on real plenoptic camera and light field microscopy datasets.
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