WiMi Developed Deep Learning-based Holographic Reconstruction Network
- Revolutionary technology that enables noise-free holographic image reconstruction without a priori knowledge
- Flexible deep learning approach with powerful training algorithms
- Potential applications in medical imaging and industrial inspection
- None.
First, the input module can receive an amplitude object, a phase object, or a hologram containing both parts of the object. In order to adapt to different types of inputs, the network prepares the corresponding dataset at each reconstruction and trains it independently. Next, the feature extraction module employs a medium-depth deep residual network consisting of a convolutional layer, a bulk normalization layer and a nonlinear activation layer. The introduction of residual units significantly improves the computational speed and accuracy of the network, and the repetition of residual units at multiple depths further enriches the data representation capability. Finally, the reconstruction module consists of a sub-pixel convolution layer, which enlarges the reduced intermediate image to its original size by a sub-pixel convolution method. Recovering images with original resolution greatly reduces computation and time.
At the heart of this new holographic reconstruction network of WiMi lies a deep learning approach that leverages the fitting and feature extraction capabilities of deep neural networks. The strength of deep learning lies in its flexibility and powerful training algorithms that can approximate any continuous function. Through the data-driven approach, the holographic reconstruction network can automatically learn the feature representation and reconstruction process of holographic images without relying on tedious a priori knowledge and manual operation. This brings great potential and convenience to the application of holographic technology.
The process of WiMi's deep learning-based holographic reconstruction network is as follows:
Data preparation: First, a hologram dataset for training and testing is prepared. The dataset may include amplitude objects, phase objects, or holograms containing both amplitude and phase information.
Network training: Train the holographic reconstruction network using a prepared dataset. Train the network to generate accurate reconstructed images by using holographic images as input. During the training process, appropriate loss functions are used to measure the difference between the reconstructed image and the real image, and optimization algorithms such as gradient descent are used to update the weights and parameters of the network.
Feature extraction: In the execution phase, input the holographic image to be reconstructed. The image can be an amplitude object, a phase object, or a holographic image containing both information. Extract key features from holographic images through the feature extraction layer of the network and encode them.
Reconstructed image generation: The feature-extracted data is decoded into a reconstructed image through the reconstruction layer of the network. Operations such as residual units, convolutional layers, and sub-pixel convolutional layers in the network will be used to generate high-quality reconstructed images step by step. These operations restore the original resolution of the image and remove unwanted zero-order and twin images.
Result output: The generated reconstructed images will be output as final results. These images will exhibit a high degree of accuracy, clarity and detail, reflecting the amplitude and phase information of the original hologram. Such reconstructed images can be used for further analysis, diagnosis and applications.
The entire execution process is end-to-end, with all steps from input to the final reconstructed image being done in a holographic reconstruction network. The network automatically learns and extracts features from the image by deep learning methods and generates high-quality reconstruction results. This data-driven approach eliminates the reliance on a priori knowledge in traditional holographic reconstruction methods and overcomes the challenges of noise processing, phase imaging, and deep image generation. The key to the entire execution process lies in the training and optimization of the network. With a large-scale training dataset and a suitable network structure, the holographic reconstruction network can learn the complex features in the hologram and generate high-quality reconstructed images. The training of the network requires a lot of computational resources and time, but once the training is complete, the image reconstruction process in the execution phase will be very efficient. Optimizing the network structure, choosing appropriate loss functions and optimization algorithms, and increasing the diversity and amount of training data can further improve the performance and robustness of the holographic reconstruction network.
In addition, WiMi's deep learning-based holographic reconstruction network has the advantage of adaptability and scalability. Due to the data-driven nature of the network, it can be adapted to different types of hologram inputs and trained and tuned as needed. This means that the technology can be used in a variety of application scenarios, including medical imaging, industrial inspection, and virtual reality. Whether it is the reconstruction of a single object or the generation of fully focused images and depth maps of multi-section objects, the holographic reconstruction network can meet the needs of different applications.
In the field of medicine, holographic reconstruction networks have great potential for application. Traditional medical imaging methods, such as CT scans and MRIs, provide detailed anatomical information but are limited in terms of fine structure and phase information in some cases. Holography can provide more comprehensive and accurate image information, which can help doctors make more accurate diagnoses and treatments. With deep learning-based holographic reconstruction networks, the process of reconstructing medical images becomes more efficient and accurate, eliminating the need for manual operations and complex computational steps, bringing greater convenience and accuracy to medical diagnosis.
In industrial inspection, holographic reconstruction networks can be applied to quality control, product inspection and defect analysis. While traditional industrial inspection methods usually require complex equipment and manual operations, holographic technology combined with the ability of deep learning can realize real-time and efficient inspection and analysis processes. Through holographic reconstruction networks, industrial companies can more quickly identify defects or problems in their products, improving production efficiency and product quality.
WiMi's deep learning-based holographic reconstruction network has a broad space for research and development. With the continuous evolution of deep learning technology and the improvement of hardware computing power, we can foresee the further improvement of holographic reconstruction network in terms of accuracy, efficiency and practicality. At the same time, the technology can also be combined with technologies from other fields, such as computer vision and natural language processing, to form more powerful and comprehensive applications. Overall, the successful development of WiMi's deep learning-based holographic reconstruction network marks the entry of holographic technology into a completely new stage. The technology not only possesses high accuracy and efficiency, but also has a wide range of application potential. With the continuous development of the technology and the expansion of applications, the holographic reconstruction network will bring more innovations and opportunities to various industries, and promote the holographic technology to a brighter future.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
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SOURCE WiMi Hologram Cloud Inc.
