Error reduction in holographic movies using a hybrid learning method in coherent neural networks

Computer Generated Holograms (CGHs) are commonly used in optical tweezers which are employed in various research fields. Frame interpolation using coherent neural networks (CNNs) based on correlation learning can be used to generate holographic movies efficiently. However, the error that appears in the interpolated CGH images need to be reduced even further so that the method with frame interpolation can be accepted for use generally. In this paper, we propose a new hybrid CNN learning method that is able to generate the movies almost just as efficiently and yet reduces even more error that is present in the generated holographic images as compared to the method based solely on correlation learning.