WiMi Developed the Deep Learning-Based Nonlinear Holographic Image Restoration

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[{"type":"text","content":"BEIJING, Sept. 18, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (\"WiMi\" or the \"Company\"), a leading global Hologram Augmented Reality (\"AR\") Technology provider, today announced that deep learning is applied to nonlinear holographic image restoration. Actively exploring the application of nonlinear holographic image restoration based on deep learning. The technology utilizes a deep neural network model, and by learning a large amount of hologram data, it can automatically learn the feature of nonlinear distortion and make accurate predictions during the restoration process. Compared with traditional methods, the deep learning-based method can better handle nonlinear distortion, improve the restoration effect, and provide a more accurate database for the subsequent analysis and application of holograms. The deep learning-based nonlinear holographic image restoration has important application value in the field of holographic image processing.\n\nThe role of deep learning-based nonlinear holographic image restoration is very important, by learning the nonlinear features of the image and the noise model, deep learning can realize more accurate image restoration and improve the quality and clarity of the image. Specifically, it is mainly reflected in the following aspects:\nFeature learning: Deep learning can learn feature representations in images through multi-layer neural networks to extract higher-level features. These features can better describe the structural information and noise models in the image, thus providing a more accurate basis for image restoration.\nNonlinear modeling: Deep learning can model noise in images by constructing complex nonlinear models. These nonlinear models can better capture the distribution and characteristics of noise in an image, leading to more accurate noise removal and image restoration.\nData drive: Deep learning is a data-driven approach that can be trained and learned 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.\nThis nonlinear holographic image restoration includes key modules such as data pre-processing, feature extraction, nonlinear transformation and reconstructed image. First, the input holographic image is pre-processed, such as denoi...

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