1 Microlens array grid estimation, light field decoding, and calibration Maximilian Schambach and Fernando Puente Leon,´ Senior Member, IEEE Karlsruhe Institute of Technology, Institute of Industrial Information Technology Hertzstr. 16, 76187 Karlsruhe, Germany fschambach,
[email protected] Abstract—We quantitatively investigate multiple algorithms lenslet images, while others perform calibration utilizing the for microlens array grid estimation for microlens array-based raw images directly [4]. In either case, this includes multiple light field cameras. Explicitly taking into account natural and non-trivial pre-processing steps, such as the detection of the mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones projected microlens (ML) centers and estimation of a regular previously discussed in the literature. To quantify the perfor- grid approximating the centers, alignment of the lenslet image mance of the algorithms, we propose an evaluation pipeline with the sensor, slicing the image into a light field and, in utilizing application-specific ray-traced white images with known the case of hexagonal MLAs, resampling the light field onto a microlens positions. Using a large dataset of synthesized white rectangular grid. These steps have a non-negligible impact on images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the quality of the decoded light field and camera calibration. the decoding and calibration of light fields taken with a Lytro Hence, a quantitative evaluation is necessary where possible. Illum camera. We observe that decoding as well as calibration Here, we will focus on the estimation of the MLA grid benefit from a more accurate, vignetting-aware grid estimation, parameters (to which we refer to as pre-calibration), which especially in peripheral subapertures of the light field.