Liu Qingwei, Chen Jing, Qiu Bingsen, Wang Yongtian, Liu Juan
Opt Express. 2023 Oct 23;31(22):35908-35921. doi: 10.1364/OE.502503.
Recent studies have demonstrated that a learning-based computer-generated hologram (CGH) has great potential for real-time, high-quality holographic displays. However, most existing algorithms treat the complex-valued wave field as a two-channel spatial domain image to facilitate mapping onto real-valued kernels, which does not fully consider the computational characteristics of complex amplitude. To address this issue, we proposed a dual-channel parallel neural network (DCPNet) for generating phase-only holograms (POHs), taking inspiration from the double phase amplitude encoding method. Instead of encoding the complex-valued wave field in the SLM plane as a two-channel image, we encode it into two real-valued phase elements. Then the two learned sub-POHs are sampled by the complementary 2D binary grating to synthesize the desired POH. Simulation and optical experiments are carried out to verify the feasibility and effectiveness of the proposed method. The simulation results indicate that the DCPNet is capable of generating high-fidelity 2k POHs in 36 ms. The optical experiments reveal that the DCPNet has excellent ability to preserve finer details, suppress speckle noise and improve uniformity in the reconstructed images.
最近的研究表明，基于学习的计算机生成全息图（CGH）在实时、高质量全息显示方面具有巨大潜力。然而，大多数现有算法将复值波场视为双通道空间域图像，以便于映射到实值内核上，这并未充分考虑复振幅的计算特性。为了解决这个问题，我们从双相位幅度编码方法中获得灵感，提出了一种用于生成纯相位全息图（POH）的双通道并行神经网络（DCPNet）。我们不是将SLM平面中的复值波场编码为双通道图像，而是将其编码为两个实值相位元素。然后，通过互补二维二元光栅对两个学习到的子POH进行采样，以合成所需的POH。进行了仿真和光学实验，以验证所提方法的可行性和有效性。仿真结果表明，DCPNet能够在36毫秒内生成高保真的2k POH。光学实验表明，DCPNet在保留更精细细节、抑制散斑噪声和提高重建图像均匀性方面具有出色的能力。
