WiMi Hologram Cloud Inc, a leading global Hologram Augmented Reality (“AR”) Technology provider, 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.
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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.
SOURCE: PRNewswire