An Improved Blur Circle Detection Method for Geometric Calibration of Multi-Focus Light Field Cameras

Abstract

Multi-focus light field cameras are increasingly gaining attention in the field of computational imaging and machine vision due to their capability to capture both spatial and angular information of light rays. Accurate calibration of the geometric parameters of multi-focus light field cameras is the prerequisite for implementing computational imaging techniques such as digital refocusing, depth reconstruction and 3D reconstruction. In this paper, an improved blur circle detection method is proposed for the calibration of multi-focus light field cameras. The Circular Hough Transform based circle detection method (CHTCDM) is used to obtain the sub-image centers accurately in white raw images. On this basis, the sub-images in the checkerboard images are classified, and the image corners are detected to determine the clusters of image corners corresponding to checkerboard corners. Then, the centers of plenoptic disc features (PDFs) are used as projection points of checkerboard corners on the light field image to calculate the camera pose. Levenberg-Marquardt method is further used to solve the calibration model. Finally, the calibration experiments of multi-focus light field cameras are carried out to evaluate the improved blur circle detection method. Experimental results indicated that the improved blur circle detection method has higher calibration accuracy of multi-focus light field camera than the blur circle detection method, while the mean reprojection error of the micro-lens centers and the image corners are 0.12 pixels and 0.33 pixels, and the mean relative distance error between adjacent checkerboards is 3.5%.

Keywords: Geometric calibration, light field camera, Sub-image center detection, corner detection, Plenoptic disc feature