A Linear Fractional Transformation Model and Calibration Method for Light Field Camera

Accurate intrinsic calibration is a crucial yet challenging prerequisite for 3D reconstruction using light field cameras. Existing calibration models typically analyze the main lens and micro lens array (MLA) in a coupled manner, resulting in high complexity and a large number of parameters. In this paper, we propose a linear fractional transformation (LFT) model that introduces a single parameter $\alpha$ to decouple the imaging processes of the main lens and the MLA. A dedicated matrix $\mathbf{H}_\alpha$ is designed to characterize the MLA projection, enabling the main lens and the MLA to be calibrated independently. The proposed calibration method consists of an analytical least-squares solution for $\mathbf{H}_\alpha$, followed by joint nonlinear refinement of all intrinsic parameters. Experimental results on both physical datasets and simulated data demonstrate that the proposed method achieves a mean translation error of $2.1\%$, outperforming the state-of-the-art, while maintaining sub-pixel reprojection accuracy. The complete codebase, including a light field simulator based on the proposed model, is openly available to the research community.