Diffraction calculations are essential in optics, including holography, optical element design, and information optics. Convolution-based diffraction calculations can be accelerated by Fourier transforms; however, they often suffer from ringing artifacts (a.k.a. Gibbs phenomena) due to the non-continuous borders of the calculation windows. Suppressing techniques for ringing artifacts have been proposed so far, but these techniques are time-consuming and use large amounts of memory. This study presents a ringing artifact reduction using the Fresnel integrals.
Holographic displays have gained attention as ideal displays because of their advantageous properties for perfectly controlling light wavefronts. However, their realization is hindered by the considerable amount of computation required for large holograms. We developed special-purpose computers for holography using field-programmable gate arrays. In this paper, we discuss strategies for next generation special-purpose computers, in which we will implement an oriented-separable convolution, which can compute holograms at high speed. This algorithm has almost the same circuit configuration as that of the previous special-purpose computers and can speed up computation by two to three orders of magnitude. In addition, we describe herein a post-processing technique using deep learning in which low-precision holograms calculated using a special-purpose computer are converted to high-precision holograms via a simple deep neural network.
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