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In the field of image processing, a holographic image is a three-dimensional image in which phase and amplitude information of light is recorded. However, due to the special properties of holographic images, they are often affected by various factors, such as light scattering, noise and distortion. Therefore, the technology of holographic image restoration has been one of the hot spots. Traditional holographic image inpainting methods are mainly based on linear models, such as filter-based methods and least-squares-based methods. However, these methods are often ineffective in dealing with nonlinear distortion.

In order to solve these problems, Micro Beauty Holography (NASDAQ:WIMI) applies deep learning technology to nonlinear holographic image restoration, and actively explores technical innovation and application of nonlinear holographic image restoration based on deep learning. Nonlinear holographic image restoration technology based on deep learning uses deep neural network model to automatically learn the feature representation of nonlinear distortion by learning a large amount of holographic image data, and make accurate prediction in the restoration process. Compared with traditional methods, the method based on deep learning can better deal with nonlinear distortion, improve the restoration effect, and provide a more accurate data base for subsequent analysis and application of holographic images. Nonlinear holographic image inpainting technology based on deep learning has important application value in the field of holographic image processing.

Deep learning plays a very important role in nonlinear holographic image restoration. By learning nonlinear features and noise models of images, deep learning can achieve more accurate image restoration and improve image quality and clarity. Specifically, the role of deep learning in nonlinear holographic image restoration is mainly reflected in the following aspects:

Feature learning: Deep learning can learn feature representations in images through multilayer neural networks to extract higher-level features. These learned features can better describe the structure information and noise model in the image, thus providing more accurate basis for image restoration.

Nonlinear modeling: Deep learning can model noise in images by building complex nonlinear models. These nonlinear models can better capture noise distributions and features in images, resulting in more accurate noise removal and image restoration.

Data-driven: Deep learning is a data-driven approach that can train and learn from large amounts of image data. This allows deep learning to learn more accurate image restoration models from data without the need to manually design complex algorithms.

The nonlinear holographic image restoration technology based on deep learning studied by WIMI micro-beauty holography includes key modules such as data preprocessing, feature extraction, nonlinear transformation and image reconstruction. First, the input holographic image is preprocessed, including denoising, downsampling and other operations, to improve the restoration effect and reduce the amount of calculation. Next, convolutional neural networks are used to extract features from the preprocessed images. These features can include information such as edges and textures for subsequent repair processes. Then, based on feature extraction, damaged or missing image information is repaired by introducing nonlinear transformation. This process is usually implemented using models such as deep neural networks, which can automatically learn the laws of nonlinear transformations by learning a large number of holographic image samples. Finally, according to the restored features and nonlinear transformation, the restored holographic image is reconstructed.

By repairing the damaged holographic image, the detail and quality of the image can be restored, and the visualization effect of the image can be improved. It is of great significance to the application and research of holographic images, and provides a strong support for the further development of related fields.

In the research of nonlinear holographic image restoration technology based on deep learning, WIMI micro-beauty holography will further explore and improve network structure optimization, dataset expansion, multimodal fusion and real-time performance improvement in the future, so as to further improve the performance and application scope of nonlinear holographic image restoration technology based on deep learning.

Current deep learning models still have some limitations when dealing with nonlinear holographic image restoration tasks. Future research at WIMI will focus on designing more efficient and accurate network structures to improve repair effectiveness and reduce computational resource consumption. For example, attention mechanisms or adaptive modules will be introduced to enhance the perception of the model to better capture detail information in the image. In addition, in order to improve the repair ability of the model, future research will also consider expanding the dataset to include more holographic image data under different scenes and different lighting conditions. In addition, consideration will be given to introducing more noise and distortion from real-world scenarios to increase the adaptability of the model to complex situations.

The task of nonlinear holographic image restoration also involves multi-modal information, including phase and amplitude information of holographic image. Future WIMI micro-beauty holograms will explore how to better integrate information from these different modalities to improve repair results. For example, or will try to introduce a method of multitask learning, learning phase and amplitude repairs at the same time to enhance the overall performance of the model. In addition, future research will focus on improving the computational efficiency of deep learning models and improving real-time performance.

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