Amp-Holo-Net enables speckle-free and high-quality coaxial amplitude-only holographic display

At present, amplitude-only holograms (AOH) are widely applied because of the high response speed and small pitch of amplitude spatial light modulators. However, AOHs are generally generated by traditional diffraction theories, and there are no effective deep learning algorithms to generate high-quality coaxial AOHs, which makes it difficult to achieve speckle-free, high-quality holographic displays using amplitude spatial light modulators. Thereby, we propose two cascaded deep neural networks, embedding physical models of the coaxial AOH to generate speckle-free and high-quality coaxial AOHs within an acceptable time, named Amp-Holo-Net, which is the first work that introduces deep learning to generate coaxial AOHs. We demonstrate the superiority of the proposed method through simulation, achieving an 81% reduction in speckle contrast of two-dimensional (2D) monochrome reconstruction and a 60% improvement in peak signal-to-noise ratio of 2D color reconstruction compared with point source method, and optical experiments are conducted to verify the validity of the proposed method. We believe our method fills the blank of AOH algorithms and paves the way for holographic displays based on amplitude spatial light modulators.