High-Accuracy Depth Estimation of Nematic Liquid Crystal Microlens Arrays Based on Convex Optimization Theory

Abstract

We have proposed an iterative feedback method based on a nematic liquid crystal microlens array (LC-MLA) light-field system, which can obtain highly accurate depth maps. A preliminary reconstruction of the all-in-focus map of the scene is produced to obtain a high-precision depth map. A natural image sparsity prior and a chromatic aberration correction are used to ensure high-quality deblurring in the presence of inaccurate depth estimation. Subsequently, a high-quality all-in-focus image is used as a constraint to obtain a high-accuracy depth estimation result. After the experiment, it can be demonstrated that the proposed method can provide good estimation results under texture-free conditions.