Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum

TL;DR

This paper introduces a learning-based method utilizing the Focal Stack Spectrum to effectively remove aliasing from entire focal stacks in light fields, maintaining consistency without explicit depth estimation.

Contribution

It presents a novel FSS reconstruction approach with a conjugate-symmetric loss, avoiding explicit depth estimation and handling large-disparity scenarios.

Findings

Outperforms previous methods on synthetic and real datasets

Effectively removes aliasing across all focal layers

Handles large-disparity scenarios successfully

Abstract

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.