Business
WiMi Developed "HoloMuxAI: Deep Learning Assisted Holographic Polarization Multiplexing" Technology
BEIJING, Nov. 24, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR")
About this update from Wimi Hologram Cloud Inc.
[{"type":"text","content":"BEIJING, Nov. 24, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (\"WiMi\" or the \"Company\"), a leading global Hologram Augmented Reality (\"AR\") Technology provider, today announced that it developed the technology \"HoloMuxAI: deep learning assisted holographic polarization multiplexing\", which is an innovation based on the framework algorithms of unsupervised deep learning computer-generated holography. The innovation of this technology lies in the application of deep learning, i.e., unsupervised learning methods to directly obtain profiles of hypersurface structures from independent holograms.\n\nWiMi's HoloMuxAI: deep learning-assisted holographic polarization multiplexing combines deep learning and hologram processing to simplify the design and generation of polarization multiplexed holograms. The following are the main components of the HoloMuxAI technology framework:\nData input: This part is used to accept the input data provided by the user, including the polarization information of the hologram and other relevant parameters.\nDeep learning: This is the core of the technology and includes a trained deep learning neural network. The architecture and parameters of the model are carefully designed to suit the hologram processing task.\nHypersurface generation: Once the deep learning model receives the input data, it generates the structural profile of the hypersurface, which is key to achieving the desired polarization multiplexing.\nHologram generation: Using the generated hypersurface structure, it is combined with the input hologram parameters to generate the final holographic polarization multiplexed image.\nOutput: The final hologram can be digitally output for display, storage or further processing.\nFeedback and improvement: The technology framework also includes feedback mechanisms to continuously improve the performance and accuracy of the deep learning model. This can be achieved by monitoring performance and user feedback in real-world applications.\nHoloMuxAI technology:\nData acquisition and preparation: First of all, it is necessary to acquire a set of independent hologram samples that contain information in different polarization states. These samples can be generated through experiments or computational simulations. Each hologram sample needs to be represented in digital form and contain ...