1. 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

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

3. 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)

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

5. The End

6. 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

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