WiMi Hologram Cloud Inc (WIMI)

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WIMI (NASDAQ: WIMI) develops an optical scanning holographic reconstruction algorithm based on convolutional neural network.

Source
https://cj.sina.com.cn/articles/view/7651844612/1c815e20402001gdrc?from=finance

May 10, 2023

With the continuous development of science and technology, optical scanning holographic imaging technology has increasingly become a popular technology in the research field. The technique creates a holographic image of a three-dimensional object by recording the interference pattern of light scattered by the object. However, due to its high complexity and large amount of data processing needs, this technology still has many challenges. To solve these problems, in recent years, deep learning techniques have been applied to optical scanning holographic reconstruction to improve the quality and speed of reconstructed holographic images.

Deep learning is a machine learning method based on neural networks, which can automatically learn features and rules from data and be used for tasks such as classification, prediction and generation. In optical scanning holographic reconstruction, deep learning can be used to optimize the reconstruction algorithm and improve the quality of reconstructed holographic images.

It is understood that the research and development team of WIMI (NASDAQ: WIMI) is developing an optical scanning holographic reconstruction algorithm based on convolutional neural network, which processes interference images through convolutional neural network (CNN) to generate high-quality holographic images. The convolution kernel is used to extract the features in the interference image, and then these features are used to generate the holographic image. Compared with traditional reconstruction algorithms, the use of CNN can reduce noise and artifacts, and improve the resolution and sharpness of reconstructed images. In addition, convolutional neural networks can speed up the reconstruction process through parallel processing and optimization algorithms. While achieving high-quality holographic reconstruction, it can also reduce computational complexity and data requirements.

According to the data, WIMI Hologram's convolutional neural network-based optical scanning holographic reconstruction algorithm uses the convolutional neural network model to perform end-to-end learning and reconstruction of optical scanning holographic images. Specifically, firstly, the collected optical scanning holographic image is fed into the convolutional neural network model as input, and then the convolutional neural network model automatically extracts high-level features from the input holographic image, and through backpropagation The algorithm continuously adjusts the network parameters to minimize the reconstruction error, and finally, outputs a high-quality holographic image reconstruction result. Its technical process includes steps such as data acquisition, data preprocessing, convolutional neural network model training, model testing, result evaluation, and optimization and improvement.

Compared with traditional holographic image processing algorithms, WIMI's optical scanning holographic reconstruction algorithm based on convolutional neural network can automatically extract features, learn more complex and advanced features from data, and obtain higher quality reconstruction results , and the whole reconstruction process is carried out in an end-to-end framework, which simplifies the process and improves the efficiency of holographic reconstruction.

The optical scanning holographic reconstruction algorithm based on convolutional neural network is a very potential technology, which has important application value and has broad application prospects in digital holographic imaging, medical image analysis, three-dimensional object recognition and other fields. It is believed that with the continuous development and improvement of technology, it will be applied in more fields and bring more convenient and efficient services to people.

In the future, WIMI will also study how to combine the convolutional neural network-based optical scanning holographic reconstruction algorithm with other computer vision and image processing technologies to achieve more comprehensive and accurate data analysis and image processing. In addition, it will also consider applying this technology to actual production, such as industrial testing, non-destructive testing and other fields, in order to achieve more intelligent and efficient production management.