A new pixel shiftmap prediction method based on Generalized Regression Neural Network

This paper proposes a new atmospheric warp estimation method based on Artificial Neural Network (ANN). We employed a Generalized Regression Neural Network (GRNN) for a-priori estimation of the upcoming warped frames using history of the previous frames. A non-rigid image registration technique is used for determining pixel shifts of the captured frames with respect to the reference frame. The proposed method is independent of the pixel-wander model. The performance of the method is evaluated using various quality metrics. Simulation results show that the proposed method provides substantial estimation of the upcoming frames with considerable errors.