WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (AR) Technology provider, announced that to study the hologram reconstruction technique based on Generative Adversarial Network (GAN), the Generative Adversarial Network Holographic Reconstruction (GAN-Holo Reconstruction) framework is proposed.
GAN is an unsupervised learning algorithm in the field of deep learning that can generate new, similar data to the input data by finding features in the training data.
The hologram reconstruction technique researched by WiMi is an emerging technique to automatically perform hologram reconstruction by generating adversarial networks. GAN can be used to rapidly generate realistic 3D holograms, thus improving the quality and generation efficiency of holograms.
A GAN usually consists of two deep neural networks called a generator and a discriminator.The generator takes random noise as input and outputs the generated hologram after a series of transformation and convolution operations, while the discriminator compares the generated image with the real hologram and provides accurate feedback to the generator to guide the generator’s improvement. In the process of continuous optimization, the generator and the discriminator will play and learn from each other, and eventually, the generative network can learn to get the distribution of the holograms, thus generating more realistic holograms.
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The biggest advantage of the GAN-based hologram reconstruction method is that it can handle complex hologram reconstruction problems, such as objects with multiple depths and reflections. At the same time, this method also has good robustness and adaptability to noise, so that even if the input image is disturbed by noise, the generated hologram can maintain high quality. In addition, this method has a high degree of automation, which can greatly improve the efficiency of hologram reconstruction.
The biggest advantage of this hologram reconstruction method is that it can handle complex hologram reconstruction problems such as multiple depths and reflected objects. At the same time, this method is also well adapted to noise, and the generated hologram can maintain high quality even if the input image is disturbed. In addition, this method has a high degree of automation, which can greatly improve the efficiency of hologram reconstruction.
The hologram reconstruction technology process for GAN studied by WiMi consists of the following main parts:
Dataset preparation. It is first necessary to prepare datasets containing a large number of holograms, which include raw data input holograms and holographically labelled image data.
Network architecture design. The GAN-based framework construction consists of two network models, the generator and the discriminator. The generator network is used to generate hologram images by mapping the input noisy data into the hologram space. The discriminator network, on the other hand, is used to evaluate the similarity between the holograms generated by the generator network and the real holograms, thus guiding the improvement of the generator network.
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Network training and optimization. During the training process, the discriminator network and the generator network will play and learn from each other, to optimize the quality of the generator network. Specifically, the generator network takes the random noise data as input and generates the corresponding hologram through a series of feature transformations and convolution operations. The discriminator network, on the other hand, compares the generated hologram with the real hologram and calculates the difference between the two. Through continuous optimization, the generator network can eventually learn the distribution of the holographic image and thus generate a more realistic holographic image.
The training process of the hologram reconstruction model of GAN studied by WIMI is controllable, and it can be adjusted by adjusting the hyper-parameters to realize the specific details of generating holograms, such as the voxel size, the resolution of the image, and so on. In addition, the GAN has the feature of iterability, which can optimize the training of the generator and discriminator through continuous modification and adjustment, to achieve higher accuracy and realism of hologram generation. The whole process of hologram reconstruction by GAN has a high degree of automation, which greatly improves the efficiency and quality of hologram reconstruction.
With the continuous development of technology, GAN-based hologram reconstruction technology has been widely used in the fields of medicine, engineering and art, realizing many innovative applications. For example, in the medical field, it is used for more accurate case diagnosis and surgery simulation training.In the field of engineering, it is used for modeling and realizing higher-quality 3D models.In the field of art, it is used in advertising, virtual reality and so on.
The GAN-based hologram reconstruction technology researched by WiMi is an important emerging technology with great development potential, and it is foreseeable that it will be more widely used in the future, including many aspects in the fields of smart city, intelligent security, engineering modelling and film and television, which has great research and development potential.
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