Output: The final hologram can be digitally output for display, storage or further processing.įeedback and improvement: The technology framework also includes feedback mechanisms to continuously improve the performance and accuracy of the deep learning model. Hologram generation: Using the generated hypersurface structure, it is combined with the input hologram parameters to generate the final holographic polarization multiplexed image. Hypersurface 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. The architecture and parameters of the model are carefully designed to suit the hologram processing task.

The following are the main components of the HoloMuxAI technology framework:ĭata 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.ĭeep learning: This is the core of the technology and includes a trained deep learning neural network. WiMi'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 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.

(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. 24, 2023 /PRNewswire/ - WiMi Hologram Cloud Inc.

Deep learning enables 3d holograms on