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Holographic imaging is a technology to record and reproduce the optical wave field of an object, which can provide three-dimensional and realistic images. However, the traditional holographic imaging technology is limited by hardware and algorithms, which can not achieve high-quality and real-time holographic image generation. With the rise of deep learning and generation countermeasure networks, people begin to try to apply GAN to hologram generation and fusion to improve the quality and fidelity of holograms.

Micro-American holography (NASDAQ:WIMI), as a company dedicated to the research and development and application of holographic technology, has laid out a multimodal holographic image fusion algorithm based on generating countermeasure network (GAN). The multimodal holographic image fusion algorithm based on generating countermeasure network (GAN) generates the generator and discriminator of the countermeasure network by training. Using the confrontation process between them, the feature extraction and fusion of the data of different modes are realized, and a realistic fusion image is generated by setting a reasonable loss function. The generator uses local detail features and global semantic features to extract the details and semantic information of the source image, while the discriminator discriminates the fused image. This algorithm can be applied to many different scenes, such as 2D / 3D object detection, semantic segmentation and tracking tasks. In addition, multimodal holographic image fusion algorithm can also be combined with other technologies, such as convolution neural network (CNN), cyclic neural network (RNN), to further improve the effect and accuracy of image fusion. The multimodal holographic image fusion algorithm based on GAN can also flexibly choose appropriate fusion strategies according to specific scenes and requirements, such as pixel-based fusion, feature-based fusion, attention-based fusion and so on.

First of all, it is necessary to preprocess the data from different sources or different modes, including image registration, image clipping, scaling, normalization and so on, in order to ensure the quality and stability of the image, and then build a countermeasure network. The task of the generator is to generate realistic holograms. It uses local detail features and global semantic features to extract the details and semantic information of the source image, and fuses these information into the hologram. The task of the discriminator is to determine whether the hologram is real. Through iterative training of the generator and discriminator, the ability of the generator to generate realistic fusion images is improved, and the ability of the discriminator to correctly distinguish between real images and fused images is also improved.

Loss function is a key component of generating confrontation network, which is used to measure the result of competition between generator and discriminator. The loss function usually consists of two parts: generation against loss and perceived loss. The generation against loss encourages the generator to generate a realistic hologram, while the perceptual loss enables the fused hologram to retain important information of the source image.

The training process of multimodal holographic image fusion algorithm based on GAN includes two stages, pre-training and fine-tuning. In the pre-training phase, only generation against loss is used for training to encourage the generator to generate realistic holograms. In the fine-tuning stage, perceptual loss is added to further improve the quality of the fused hologram. Then the quality of the fused image is evaluated by the standard evaluation index, and the parameters of the generator and discriminator are further optimized and adjusted according to the evaluation results.

The multimodal holographic image fusion algorithm based on GAN studied by WIMI micro-beauty holography can generate holograms with richer information content by fusing multi-modal image data, so as to improve the quality and reliability of holograms. It can be widely used in augmented reality, virtual reality, security monitoring and other fields. With the continuous development of deep learning technology, the multimodal holographic image fusion algorithm based on GAN has great potential in the future, which will bring important breakthroughs and innovations in the field of holography and intelligent interaction, and bring more convenience and value to people's life and work.

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