Improved blur circle detection method for geometric calibration of multifocus light field cameras

Multifocus light field cameras are increasingly gaining attention in the field of computational imaging and machine vision due to their ability to capture spatial and angular information of light rays. Accurate calibration of the geometric parameters of multifocus light field cameras is the prerequisite for implementing computational imaging techniques, such as digital refocusing, depth reconstruction, and three-dimensional reconstruction. We propose an improved blur circle detection method for the calibration of multifocus light field cameras. The circular Hough transform-based circle detection method is used to obtain the subimage centers accurately in white raw images. On this basis, the subimages 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 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 multifocus 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 multifocus light field camera than the blur circle detection method, while the mean reprojection error of the microlens centers and the image corners are 0.12 and 0.33 pixels, and the mean relative distance error between adjacent checkerboards is 2.53%.