Microlens array grid estimation, light field decoding, and calibration

TL;DR

This paper evaluates and improves algorithms for estimating microlens array grids in light field cameras, demonstrating that accounting for vignetting effects enhances calibration accuracy, especially at the periphery.

Contribution

Introduces a new vignetting-aware microlens grid estimation method and an evaluation pipeline using synthetic images for performance comparison.

Findings

Vignetting-aware estimation outperforms previous methods.

Improved calibration accuracy, especially at the light field edges.

Validation on real Lytro images confirms benefits of the proposed method.

Abstract

We quantitatively investigate multiple algorithms for microlens array grid estimation for microlens array-based light field cameras. Explicitly taking into account natural and mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones previously discussed in the literature. To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing application-specific ray-traced white images with known microlens positions. Using a large dataset of synthesized white images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the decoding and calibration of light fields taken with a Lytro Illum camera. We observe that decoding as well as calibration benefit from a more accurate, vignetting-aware grid estimation, especially in peripheral subapertures of the light field.