Geometric Calibration of Micro-Lens-Based Light Field Cameras Using Line Features

We present a novel method for the geometric calibration of micro-lens-based light field cameras. Accurate geometric calibration is the basis of various applications. Instead of using sub-aperture images, we directly utilize raw images for calibration. We select appropriate regions in raw images and extract line features from micro-lens images in those regions. For the entire process, we formulate a new projection model of a micro-lens-based light field camera, which contains a smaller number of parameters than previous models. The model is transformed into a linear form using line features. We compute the initial solution of both the intrinsic and the extrinsic parameters by a linear computation and refine them via non-linear optimization. Experimental results demonstrate the accuracy of the correspondences between rays and pixels in raw images, as estimated by the proposed method.

1 Introduction

Computational cameras have been developed to encode information that cannot be captured by traditional cameras. The additional information is effectively used to convert raw data into useful images, such as deblurred images or depth maps [1], [2], [3], [4], [5]. In recent years, light field cameras (or plenoptic cameras) have been commercialized and highlighted as a next-generation camera.