In this thesis, an innovative algorithm for improving the accuracy of variational space-time stereoscopic reconstruction of ocean surfaces is presented. The space-time reconstruction method, developed based on stereo computer vision principles and variational optimization theory, takes videos captured by synchronized cameras as inputs and produces the shape and superficial pattern of an overlapped region of interest as outputs. These outputs are designed to be the minimizers of the variational optimization framework and are dependent on the estimation of the camera parameters. Therefore, from the perspective of computer vision, the proposed algorithm adjusts the estimation of camera parameters to lower the disagreement between the reconstruction and 2-D camera recordings. From a mathematical perspective, since the minimizers of the variational framework are determined by a set of partial differential equations (PDEs), the algorithm modifies the coefficients of the PDEs based on the current numerical solutions to reduce the minimum of the optimization framework. Our algorithm increases the tolerance to the errors of camera parameters, so the joint operations of our algorithm and the variational reconstruction method can generate accurate space-time models even using videos captured by perturbed cameras as input. This breakthrough prompts the realization of ocean surface reconstruction using videos filmed by remotely-controlled helicopters in the future. A number of techniques, technical or theoretical, are explored to fulfill the development and implementation of the algorithm and relative computation issues. The effectiveness of the proposed algorithm is validated through the statistics applied to real ocean surface reconstructions of data collected from an offshore platform at the Crimean Peninsula, the Black Sea. Moreover, synthetic data generated using computer graphics are customized to simulate various situations that are not recorded in the Crimea dataset for the demonstration of the algorithm.

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