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Hologram is a kind of image technology which can show reality in three-dimensional space, which can be realized by recording and reproducing the light field information of objects. However, because the generation of hologram requires a lot of computing resources and complex algorithms, it is limited in practical application.

Generated countermeasure network (GAN) is a powerful deep learning model, which has achieved remarkable results in the field of image generation and image processing. It is reported that Weimei holography (NASDAQ:WIMI) will be applied to the generation of holograms, and its research and development team is studying the hologram generation technology based on generation countermeasures network (GAN).

GAN is a neural network model composed of generator and discriminator, which realizes image generation through adversarial learning. The generator is used to generate the interference pattern of the hologram, and the discriminator is used to judge whether the generated hologram is realistic or not. Through continuous iterative training of the generator and discriminator, more realistic and high-quality holograms can be obtained, which provides a new possibility for the application of holograms.

The application of holographic image generation technology based on generation countermeasure network (GAN) studied by WIMI micro-beauty holography can be divided into the following steps:

Data preparation: first of all, holographic data sets need to be prepared for training GAN. These data sets should contain holographic images with diversity in order to generate countermeasure networks that can learn the characteristics and structures of holographic images.

Build-generate confrontation network: next, you need to build build-generate confrontation network, including generator and discriminator. The goal of the generator is to generate an image as close as possible to the real hologram, which is responsible for generating a realistic hologram, while the discriminator is responsible for judging whether the generated image is real or not. its goal is to distinguish between real and false holograms as accurately as possible, and it evaluates the authenticity of the input images by classifying them. The generator and the discriminator are continuously optimized through confrontation training in order to generate realistic holograms.

Confrontation training: the generated confrontation network is trained using the prepared holographic data set. The generator and discriminator are trained through adversarial learning. the generator generates a hologram and passes it to the discriminator for judgment and classification. The discriminator gives feedback according to the authenticity of the generated image and passes it to the generator to optimize and update its own parameters to make the generated image closer to the real holographic image. Through repeated iterative training, the generator and discriminator gradually improve the performance, the generated hologram gradually becomes more realistic, and the image quality is also gradually improved.

Evaluation and tuning: after the training is completed, the generated hologram needs to be evaluated and tuned. First, the model of the generated confrontation network is evaluated, and the fidelity and accuracy of the generated image are evaluated. According to the evaluation results, the parameters of the generation countermeasure network are optimized to further improve the generation quality of the hologram.

GAN can compete and cooperate with each other by training the two networks of generator and discriminator. this mechanism can help the generator network learn better image processing ability, so that the holographic image generation technology based on generation countermeasure network has more real holographic image generation ability and higher quality holographic image generation effect. these advantages make the holographic image generation technology based on GAN have a wide application prospect. It has a wide range of applications, including medicine, education, entertainment and other fields.

However, at present, the holographic image generation technology based on generation countermeasure network still has some limitations in generating realistic images, such as the details of the images are not clear enough, the colors are not bright enough, and so on. In the future, the research of WIMI micro-beauty holography will focus on improving the structure and training algorithm of the generating network in order to improve the quality and realism of the generated image.

Holographic image generation technology based on generation countermeasure network requires a lot of computing resources and time. In the future, WIMI micro-beauty holography will improve the speed and efficiency of holographic image generation by optimizing network structure and algorithm, as well as using parallel computing and hardware acceleration. In addition, it will also explore how to introduce variational self-encoder and other methods to enable the generation network to generate more diversified holograms and increase the diversity of generated images to meet the needs of different users.

At present, the holographic image generation technology based on generation countermeasure network is mainly used in virtual reality, augmented reality and other fields. In the future, WIMI micro-beauty holography will be extended to more application fields, such as medicine, engineering design, literary creation and other fields, providing more possibilities for these fields.

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