  The paper aims at removing the aliasing effects for the whole focal stack
generated from a sparse 3D light field, while keeping the consistency across
all the focal layers.We first explore the structural characteristics embedded
in the focal stack slice and its corresponding frequency-domain representation,
i.e., the focal stack spectrum (FSS). We also observe that the energy
distribution of FSS always locates within the same triangular area under
different angular sampling rates, additionally the continuity of point spread
function (PSF) is intrinsically maintained in the FSS. Based on these two
findings, we propose a learning-based FSS reconstruction approach for one-time
aliasing removing over the whole focal stack. What's more, a novel
conjugate-symmetric loss function is proposed for the optimization. Compared to
previous works, our method avoids an explicit depth estimation, and can handle
challenging large-disparity scenarios. Experimental results on both synthetic
and real light field datasets show the superiority of the proposed approach for
different scenes and various angular sampling rates.
