Color And Angular Reconstruction Of Light Fields From Incomplete-Color Coded Projections

We present a simple variational approach for reconstructing color light fields (LFs) in the compressed sensing (CS) framework with very low sampling ratio, using both coded masks and color filter arrays (CFAs). A coded mask is placed in front of the camera sensor to optically modulate incoming rays, while a CFA is assumed to be implemented at the sensor level to compress color information. Hence, the LF coded projections, operated by a combination of the coded mask and the CFA, measure incomplete color samples with a three-times-lower sampling ratio than reference methods that assume full color (channel-by-channel) acquisition. We then derive adaptive algorithms to directly reconstruct the light field from raw sensor measurements by minimizing a convex energy composed of two terms. The first one is the data fidelity term which takes into account the use of CFAs in the imaging model, and the second one is a regularization term which favors the sparse representation of light fields in a specific transform domain. Experimental results show that the proposed approach produces a better reconstruction both in terms of visual quality and quantitative performance when compared to reference reconstruction methods that implicitly assume prior color interpolation of coded projections.