Abstract
Light field imaging technology can obtain three-dimensional (3D) information of a test surface in a single exposure. Traditional light field reconstruction algorithms not only take a long time to trace back to the original image, but also require the exact parameters of the light field system, such as the position and posture of a microlens array (MLA), which will cause errors in the reconstructed image if these parameters cannot be precisely obtained. This paper proposes a reconstruction algorithm for light field imaging based on the point spread function (PSF), which does not require prior knowledge of the system. The accurate PSF derivation process of a light field system is presented, and modeling and simulation were conducted to obtain the relationship between the spatial distribution characteristics and the PSF of the light field system. A morphology-based method is proposed to analyze the overlapping area of the subimages of light field images to identify the accurate spatial location of the MLA used in the system, which is thereafter used to accurately refocus light field imaging. A light field system is built to verify the algorithm’s effectiveness. Experimental results show that the measurement accuracy is increased over 41.0% compared with the traditional method by measuring a step standard. The accuracy of parameters is also improved through a microstructure measurement with a peak-to-valley value of 25.4% and root mean square value of 23.5% improvement. This further validates that the algorithm can effectively improve the refocusing efficiency and the accuracy of the light field imaging results with the superiority of refocusing light field imaging without prior knowledge of the system. The proposed method provides a new solution for fast and accurate 3D measurement based on a light field.
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