Plenoptic system identification using random calibration patterns

We consider the calibration problem in plenoptic systems with the goal to estimate the system matrix given a set of known calibration patterns and their corresponding observations. The proposed calibration method is divided into a subspace identification step and a parameter estimation step. Exploiting newly discovered properties of the simulated system matrix, such as its low rank and the equality of its row and column spaces, we propose to identify the requisite subspace by computing the dominant left singular vectors of the observation matrix. Subsequently, we project each row of the observation matrix onto the subspace determined by these singular vectors. Our simulation results show that our method is able to accurately estimate the system matrix and significantly outperform a state-of-the-art low-rank recovery algorithm.