Reduction of Aliasing Artifacts by Sign Function Approximation in Light Field Depth Estimation Based on Foreground–Background Separation

Abstract

A sign function approximation method for depth from light field (DFLF) based on the foreground–background separation (FBS) is proposed. From a signal processing viewpoint, the FBS-based method can be considered as a bridge between the cost-based DFLF methods and depth model-based ones. The proposed sign function approximation corresponds to the winner-takes-all (WTA) approaches as the cost-based methods do. Experimental results on the synthetic images show that the proposed method reasonably performs in terms of the mean squared error as the state-of-the-art methods do. Especially, by using a suitable WTA method in the framework of our previous FBS-based DFLF work, the proposed method effectively reduces the angular aliasing artifacts in the resulting disparity maps of both the synthetic and real images.