Microlens array grid estimation, light field decoding, and calibration
This work addresses a domain-specific problem for light field camera users, offering incremental improvements in grid estimation accuracy.
The paper tackles the problem of microlens array grid estimation for light field cameras by proposing a new method that accounts for vignetting effects, which outperforms previous algorithms and improves decoding and calibration accuracy, especially in peripheral subapertures.
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.