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Digital holographic imaging is a powerful technique that can provide wavefront information of a three-dimensional object for biological and industrial applications. However, due to the constraint and cost of imaging sensors, the acquired digital hologram is limited in terms of pixel count, thus affecting the resolution in holographic reconstruction. To overcome this constraint, in this paper we propose a deep learning-based method to super-resolve holograms and to improve the quality of low-resolution holograms by training a convolutional neural network with large-scale data for resolution enhancement. Moreover, this algorithm can be broadly adapted to enhance the space-bandwidth product of a holographic imaging system without the need of any advanced hardware. We experimentally validate its capability using a lens-free off-axis holographic system, and compare the performance of various loss functions and interpolation methods in training such a network. [ABSTRACT FROM AUTHOR]

A prototype autostereoscopic three-dimensional (3D) light-emitting diode (LED) display using a cylindrical diffractive optical elements (C-DOEs) sheet as the optical steering element is proposed. The operation of the system and the calculation method of the system parameters are described in detail. The DOEs sheet is placed from a distance from the LED display panel which is five times smaller of the existing technology, and the column spacings of the pixels of the LED display panel are nonequal in order to equalize the distance between the viewing zones. The prototype has a 1.33 m^2 display panel, 384\times144 resolution and a horizontal field of view of 60^\circ. The experimental result shows that the proposed method is a potential autostereoscopic technology for the large area LED displays. [ABSTRACT FROM PUBLISHER]