Blur Aware Calibration of Multi-Focus Plenoptic Camera
This addresses calibration challenges for MFPC users, but it appears incremental as it builds on existing methods with a new feature.
The paper tackles the calibration problem for Multi-Focus Plenoptic Cameras by introducing a blur-aware feature and optimization process, achieving validated effectiveness through experiments.
This paper presents a novel calibration algorithm for Multi-Focus Plenoptic Cameras (MFPCs) using raw images only. The design of such cameras is usually complex and relies on precise placement of optic elements. Several calibration procedures have been proposed to retrieve the camera parameters but relying on simplified models, reconstructed images to extract features, or multiple calibrations when several types of micro-lens are used. Considering blur information, we propose a new Blur Aware Plenoptic (BAP) feature. It is first exploited in a pre-calibration step that retrieves initial camera parameters, and secondly to express a new cost function for our single optimization process. The effectiveness of our calibration method is validated by quantitative and qualitative experiments.