Abstract

In this paper, we propose an optimized algorithm to estimate the depth information in the 4D light field data. Our scheme has the advantage of conciseness compared to the traditional epipolar-plane image analysis method. First, we have analyzed the depth resolution properties of light field data not mentioned by the previous researchers. In the depth estimation process, epipolar analysis is confined in a small range to reduce the running time, combining with a regression test to reduce estimation error. Occlusion condition is especially dealt with by recognizing object margin. To test the accuracy of our algorithm, we use a benchmark dataset to evaluate the output depth result. We get a competitive result in the estimation error evaluation and prevailing runtime result compared to that of baseline algorithms. Owing to the high performance, this algorithm can be used in real-time depth recognition with the aid of parallel computing.

© 2017 Optical Society of America

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