Computational Plenoptic Imaging Gordon Wetzstein1 Ivo Ihrke2 Douglas Lanman3 Wolfgang Heidrich1 1University of British Columbia 2Saarland University 3MIT Media Lab VIII. Discussion Eurographics 2011 – State of the Art Report

Survey of plenoptic image acquisition Summary Survey of plenoptic image acquisition Classification based on plenoptic dimension & hardware setup Also see computational photography

Most approaches use fixed plenoptic resolution tradeoffs Observations Most approaches use fixed plenoptic resolution tradeoffs Strong correlations between plenoptic dimensions Need for sophisticated reconstruction techniques (e.g. compressive sensing)

Future Directions – Exploit Plenoptic Redundancy Plenoptic datasets Simulate acquisition & reconstruction Explore redundancies [Wetzstein et al. 11]

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Future Directions – Exploit Plenoptic Redundancy Explore plenoptic priors – mathematical formulations for correlations between and within dimensions Common practice in Color demosaicking Extended DOF and light field acquisition (dimensionality gap prior) [Levin et al. 09,10] Extend to time, polarization, plenoptic manifolds

Future Directions – Unified Plenoptic Reconstruction Unified reconstruction in terms of Domain (image space vs. Fourier) Plenoptic dimension [Ihrke et al. 10]