Correction of Barrel Distortion in Fisheye Lens Images Using Image-Based Estimation of Distortion Parameters

Abstract

Images acquired by a fisheye lens camera contain geometric distortion that results in deformation of the object's shape. To correct the lens distortion, existing methods use prior information, such as calibration patterns or lens design specifications. However, the use of a calibration pattern works only when an input scene is a 2-D plane at a prespecified position. On the other hand, the lens design specifications can be understood only by optical experts. To solve these problems, we present a novel image-based algorithm that corrects the geometric distortion. The proposed algorithm consists of three stages: i) feature detection, ii) distortion parameter estimation, and iii) selection of the optimally corrected image out of multiple corrected candidates. The proposed method can automatically select the optimal amount of correction for a fisheye lens distortion by analyzing characteristics of the distorted image using neither prespecified lens design parameters nor calibration patterns. Furthermore, our method performs not only on-line correction by using facial landmark points, but also off-line correction described in subsection III-C. As a result, the proposed method can be applied to a virtual reality (VR) or augmented reality (AR) camera with two fisheye lenses in a field-of-view (FOV) of 195°, autonomous vehicle vision systems, wide-area visual surveillance systems, and unmanned aerial vehicle (UAV) cameras.