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.
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 following are the main components of the
HoloMuxAI technology framework:
Data 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.
Deep 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.
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.
Hologram generation: Using the generated hypersurface structure,
it is combined with the input hologram parameters to generate the
final holographic polarization multiplexed image.
Output: The final hologram can be digitally output for display,
storage or further processing.
Feedback 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.
HoloMuxAI technology:
Data 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
Jones matrix information.
Deep learning network design: Next, a deep learning neural
network is used to learn the hypersurface structure profiles from
these hologram samples. This neural network can be constructed
using a convolutional neural network (CNN) and recurrent neural
network (RNN) for extracting and learning features and patterns
from the holograms.
Training the neural network: Using the prepared hologram
samples, the deep learning network will be trained. The goal of
training is to enable the network to predict the structural profile
of the hypersurface based on the input hologram data. This requires
a large amount of labelled data and an appropriate loss function to
ensure that the network learns to reconstruct the hypersurface
correctly.
Model validation and optimization: After training is completed,
the model is validated to ensure that it performs as required. If
needed, the model can be further optimized and tuned to improve its
accuracy and generalization.
Practical application: Once the model has been trained and
validated, it can be applied to actual hologram design tasks. Users
only need to provide the required polarization information and
other relevant parameters, and then use the deep learning model to
generate the corresponding hypersurface structures to achieve the
desired holographic polarization multiplexing.
WiMi's HoloMuxAI technology combines the power of deep learning,
making it possible to automate the generation of complex
holographic polarization multiplexing images without the need for
manual design and complex physical calculations. The strength of
the approach lies in its versatility and scalability. It can be
applied not only to polarization multiplexed holograms, but can
also be extended to other multiplexed holograms, with the potential
for a wider range of applications.
WiMi's HoloMuxAI: deep learning-assisted holographic
polarization multiplexing technology has great potential for
development. First of all, this technology represents a
breakthrough in the field of information processing and display,
and creates new opportunities for the widespread application of
holography. It will transform areas such as virtual reality,
medical imaging, communications and data storage, enabling us to
better utilize polarization information to provide clearer and more
vivid images and data transmission. Second, the successful
implementation of WiMi's HoloMuxAI technology will also facilitate
the application of deep learning in other fields. This
information-driven approach can be applied to a wider range of
problems, from materials science to autonomous driving, from
natural language processing to bioinformatics, bringing the power
of deep learning to more fields and driving technological
innovation.
WiMi's HoloMuxAI: deep learning-assisted holographic
polarization multiplexing is a revolutionary technology that breaks
through the limitations of traditional hologram technology through
a deep learning approach. It not only simplifies the design and
generation of holograms, but also brings new possibilities to the
field of information processing and display. With the continuous
development and application of this technology, there will be more
exciting innovations and advances, which will profoundly affect our
daily life and scientific research. The invention of this
technology will unlock more potential in holographic technology and
bring new opportunities and innovations in the field of holographic
information processing and display.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud
comprehensive technical solution provider that focuses on
professional areas including holographic AR automotive HUD
software, 3D holographic pulse LiDAR, head-mounted light field
holographic equipment, holographic semiconductor, holographic cloud
software, holographic car navigation and others. Its services and
holographic AR technologies include holographic AR automotive
application, 3D holographic pulse LiDAR technology, holographic
vision semiconductor technology, holographic software development,
holographic AR advertising technology, holographic AR entertainment
technology, holographic ARSDK payment, interactive holographic
communication and other holographic AR technologies.
Safe Harbor Statements
This press release contains "forward-looking statements" within
the Private Securities Litigation Reform Act of 1995. These
forward-looking statements can be identified by terminology such as
"will," "expects," "anticipates," "future," "intends," "plans,"
"believes," "estimates," and similar statements. Statements that
are not historical facts, including statements about the Company's
beliefs and expectations, are forward-looking statements. Among
other things, the business outlook and quotations from management
in this press release and the Company's strategic and operational
plans contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
Further information regarding these and other risks is included
in the Company's annual report on Form 20-F and the current report
on Form 6-K and other documents filed with the SEC. All information
provided in this press release is as of the date of this press
release. The Company does not undertake any obligation to update
any forward-looking statement except as required under applicable
laws.
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SOURCE WiMi Hologram Cloud Inc.