WiMi Is Researching Generative Adversarial Network-based Hologram Generation

BEIJING, Oct. 27, 2023 /PRNewswire/ — WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced that its R&D team is working on the generative adversarial network (GAN)-based holographic image generation. It is going to apply generative adversarial networks to holographic image generation.

GAN is a neural network model consisting of a generator and a discriminator for image generation through adversarial learning. The generator is used to generate the interference pattern of the hologram, while the discriminator is used to determine whether the generated hologram is realistic or not. By iteratively training the generator and the discriminator, more realistic and high-quality holograms can be obtained, providing new possibilities for the application of holograms.
The application of GAN-based holographic image generation researched by WiMi can be divided into the following steps:
Data preparation: First, the hologram datasets for training the GAN need to be prepared, and these datasets should contain holograms with diversity so that the generative adversarial network can learn the features and structure of the holograms.
Building a GAN: Including building a generator and a discriminator. The goal of the generator is to generate images as close as possible to the real holograms, and it is responsible for generating realistic holograms, while the discriminator is responsible for judging whether the generated images are real or not, and its goal is to distinguish real and fake holograms as accurately as possible, and it evaluates the authenticity of the input images by classifying them. The generator and the discriminator are constantly optimized by adversarial training to achieve the goal of generating realistic holograms.
Adversarial training: the GAN is trained using the prepared hologram dataset. The generator and the discriminator are trained by means of adversarial learning. The generator generates holograms and passes them to the discriminator for judgment and classification. The discriminator gives feedback based on the realism of the generated image and passes it to the generator to optimize and update its parameters to make the generated image closer to the real hologram. Through repeated training, the generator and the discriminator gradually improve their performance, and the generated holograms gradually become more realistic and the image quality gradually improves.
Evaluation and tuning: After the training is completed, the generated holograms need to be evaluated and tuned. The generative adversarial network is first evaluated to assess the degree of realism and accuracy of the generated images. According to the evaluation results, the parameters of the generative adversarial network are tuned to further improve the quality of hologram generation.
The coordination between the generator and the discriminator can help the generator network to learn a better image processing ability, so that the holographic image generation technology based on generative adversarial network has a more realistic holographic image generation ability, higher quality holographic image generation effect, and these advantages make the holographic image generation technology based on the GAN in has a wide range of prospects for application in a wide range of application areas, including medicine, education, entertainment and other fields.
However, the current holographic image generation based on GAN still has some limitations in generating realistic images, such as the image details are not clear enough and the colors are not vivid enough. Future, WiMi will focus on improving the structure and training algorithm of the generative network to improve the quality and realism of the generated images.
Hologram generation based on GAN requires a large amount of computational resources and time. In the future, WiMi will improve the speed and efficiency of hologram generation by optimizing the network structure and algorithms, as well as by using techniques such as parallel computing and hardware acceleration. In addition, it will explore how to make the generation network capable of generating more diverse holograms by introducing methods such as variational autoencoders, increasing the diversity of generated images and meeting the needs of different users.
Currently, GAN-based hologram generation is mainly used in virtual reality, augmented reality and other fields. In the future, WiMi will be expanded to more application fields, such as applying it to medicine, engineering design, literary and artistic creation and other fields, providing more possibilities for these fields.
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.

View original content:https://www.prnewswire.com/news-releases/wimi-is-researching-generative-adversarial-network-based-hologram-generation-301969966.html
SOURCE WiMi Hologram Cloud Inc. 

BEIJING, Oct. 27, 2023 /PRNewswire/ — WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced that its R&D team is working on the generative adversarial network (GAN)-based holographic image generation. It is going to apply generative adversarial networks to holographic image generation.

GAN is a neural network model consisting of a generator and a discriminator for image generation through adversarial learning. The generator is used to generate the interference pattern of the hologram, while the discriminator is used to determine whether the generated hologram is realistic or not. By iteratively training the generator and the discriminator, more realistic and high-quality holograms can be obtained, providing new possibilities for the application of holograms.

The application of GAN-based holographic image generation researched by WiMi can be divided into the following steps:

Data preparation: First, the hologram datasets for training the GAN need to be prepared, and these datasets should contain holograms with diversity so that the generative adversarial network can learn the features and structure of the holograms.

Building a GAN: Including building a generator and a discriminator. The goal of the generator is to generate images as close as possible to the real holograms, and it is responsible for generating realistic holograms, while the discriminator is responsible for judging whether the generated images are real or not, and its goal is to distinguish real and fake holograms as accurately as possible, and it evaluates the authenticity of the input images by classifying them. The generator and the discriminator are constantly optimized by adversarial training to achieve the goal of generating realistic holograms.

Adversarial training: the GAN is trained using the prepared hologram dataset. The generator and the discriminator are trained by means of adversarial learning. The generator generates holograms and passes them to the discriminator for judgment and classification. The discriminator gives feedback based on the realism of the generated image and passes it to the generator to optimize and update its parameters to make the generated image closer to the real hologram. Through repeated training, the generator and the discriminator gradually improve their performance, and the generated holograms gradually become more realistic and the image quality gradually improves.

Evaluation and tuning: After the training is completed, the generated holograms need to be evaluated and tuned. The generative adversarial network is first evaluated to assess the degree of realism and accuracy of the generated images. According to the evaluation results, the parameters of the generative adversarial network are tuned to further improve the quality of hologram generation.

The coordination between the generator and the discriminator can help the generator network to learn a better image processing ability, so that the holographic image generation technology based on generative adversarial network has a more realistic holographic image generation ability, higher quality holographic image generation effect, and these advantages make the holographic image generation technology based on the GAN in has a wide range of prospects for application in a wide range of application areas, including medicine, education, entertainment and other fields.

However, the current holographic image generation based on GAN still has some limitations in generating realistic images, such as the image details are not clear enough and the colors are not vivid enough. Future, WiMi will focus on improving the structure and training algorithm of the generative network to improve the quality and realism of the generated images.

Hologram generation based on GAN requires a large amount of computational resources and time. In the future, WiMi will improve the speed and efficiency of hologram generation by optimizing the network structure and algorithms, as well as by using techniques such as parallel computing and hardware acceleration. In addition, it will explore how to make the generation network capable of generating more diverse holograms by introducing methods such as variational autoencoders, increasing the diversity of generated images and meeting the needs of different users.

Currently, GAN-based hologram generation is mainly used in virtual reality, augmented reality and other fields. In the future, WiMi will be expanded to more application fields, such as applying it to medicine, engineering design, literary and artistic creation and other fields, providing more possibilities for these fields.

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.

View original content:https://www.prnewswire.com/news-releases/wimi-is-researching-generative-adversarial-network-based-hologram-generation-301969966.html

SOURCE WiMi Hologram Cloud Inc.

 

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