Compressive Light Field Imaging with Weighted Random Projections

Traditional light field imagers do not exploit the inherent spatio-angular correlations in light field of natural scenes towards reducing the number of measurements and minimizing the spatio-angular resolution trade-off. Here we describe a compressive light field imager that utilizes the prior knowledge of sparsity/compressibility along the spatial dimension of the light field to make compressive measurements. The reconstruction performance is analyzed for three choices of measurement bases: wavelet, random, and weighted random using a simulation study. We find that the weighted random bases outperforms both the coherent wavelet basis and the incoherent random basis on a light field data set. Specifically, the simulation study shows that the weighted random basis achieves 44% to 50% lower reconstruction error compared to wavelet and random bases for a compression ratio of three.