  Correlation Plenoptic Imaging (CPI) is a novel technological imaging modality
enabling to overcome drawbacks of standard plenoptic devices, while preserving
their advantages. However, a major challenge in view of real-time application
of CPI is related with the relevant amount of required frames and the
consequent computational-intensive processing algorithm. In this work, we
describe the design and implementation of an optimized processing algorithm
that is portable to an efficient computational environment and exploits the
highly parallel algorithm offered by GPUs. Improvements by a factor ranging
from 20x, for correlation measurement, to 500x, for refocusing, are
demonstrated. Exploration of the relation between the improvement in
performance achieved and actual GPU capabilities, also indicates the
feasibility of near-real time processing capability, opening up to the
potential use of CPI for practical real-time application.
