The paper aims at removing the aliasing effects of the whole focal stack generated from a sparse-sampled {4D} 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 observe that the energy distribution of the FSS always resides within the same triangular area under different angular sampling rates, additionally the continuity of the Point Spread Function (PSF) is intrinsically maintained in the FSS. Based on these two observations, we propose a learning-based FSS reconstruction approach for one-time aliasing removing over the whole focal stack. Moreover, 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.
翻译:本文旨在去除从稀有的 {4D} 光场生成的整个焦堆的别名效果,同时保持所有焦层的一致性。 我们首先探讨焦堆切片及其相应的频率域表层结构特征, 即焦点堆积光谱( FSS ) 。 我们观察到, FSS 的能量分布总是在不同的角取样率下位于同一个三角区域, 而在FSS 中, 点块扩展函数( PSF) 的连续性是内在的。 基于上述两点观察, 我们提出一个基于学习的FSS 重建方法, 用于一次性别名清除整个焦块。 此外, 为优化工作提出了一个新的共振- 度损失函数。 与以前的工作相比, 我们的方法避免了清晰的深度估计, 并能够处理挑战大差异的假设。 合成和真实光场数据集的实验结果显示了不同场景和不同角取样率的拟议方法的优越性。