Computer generated holograms (CGHs) are widely used in optical tweezers, which will be employed in various research fields. We previously proposed an efficient generation method of CGH movies based on frame interpolation using coherent neural networks (CNNs) to reduce the high calculation cost of three-dimensional CGHs. At the same time, however, we also found that the quality observed in the interpolated CGH images needed to be improved even further so that the method could be accepted for general use. We report a successful error reduction in interpolated images by developing a new learning method of CNNs. We reduce the error by combining locally connected correlation learning and steepest descent learning in a sequential manner.
© 2008 Optical Society of America
Original Manuscript: January 23, 2008
Revised Manuscript: June 10, 2008
Manuscript Accepted: June 23, 2008
Published: September 30, 2008
Vol. 3, Iss. 12 Virtual Journal for Biomedical Optics
Chor Shen Tay, Ken Tanizawa, and Akira Hirose, "High-quality frame interpolation in computer generated holographic movies using coherent neural networks with a hybrid learning method," Appl. Opt. 47, 5221-5228 (2008)