An Artificial Neural Network Approach for Underwater Warp Prediction

This paper presents an underwater warp estimation approach based on generalized regression neural network (GRNN). The GRNN, with its function approximation feature, is employed for a-priori estimation of the upcoming warped frames using history of the previous frames. An optical flow technique is employed for determining the dense motion fields of the captured frames with respect to the first frame. The proposed method is independent of the pixel-oscillatory model. It also considers the interdependence of the pixels with their neighborhood. Simulation experiments demonstrate that the proposed method is capable of estimating the upcoming frames with small errors.