WiMi Hologram Cloud Inc., a global Hologram Augmented Reality (“AR”) Technology provider, announced the development of a new image processing technology based on artificial intelligence and machine learning. The technology can improve the accuracy and efficiency of image processing and extend the range of applications for image processing. The technology processes images through deep learning techniques, using convolutional neural networks (CNNs) to extract features from images. These features can be used for image classification, target detection, and image segmentation tasks. WiMi introduces deep learning techniques such as Residual Networks (ResNet) and Attention Mechanisms to improve the accuracy further. By using CNNs and Recurrent Neural Networks (RNN) for deep learning and feature extraction, WiMi achieves automated classification and recognition of images. WiMi also uses techniques such as segmentation networks to automate image segmentation and localisation and achieves automated processing and analysis of specific image regions.
In addition, WiMi uses Generative Adversarial Networks (GAN), a deep learning model that learns how to generate images with a sense of realism to enhance the quality of images. Using conditional GAN (cGAN) allows the generation of images that match the user’s needs. This allows the technology developed by WiMi to provide more precise control over the image during image processing while improving the quality of the image.
WiMi has developed this technology as an integrated system, integrating deep learning techniques and algorithms with additional tools and interfaces. This helps users to use this system quickly and implement their application requirements. It can be used for image processing, analysis, and prediction. The system can process various types of image data, such as 2D images and 3D point cloud data, including images from natural scenes, medical images, and remote sensing images. Moreover, the system supports processing images of different modalities, such as grayscale, colour, and multispectral images. In addition, the processing flow can be adaptively adjusted to accommodate different resolutions when processing images with different resolutions. The system is also powerfully scalable and adaptable: by combining distributed computing and heterogeneous computing, efficient parallel computing and data exchange can be achieved, improving processing speed and efficiency. And the system applies to various hardware platforms, including CPUs, GPUs, and FPGAs, and can be deployed in multiple operating systems and development environments.
WiMi will be able to be applied to several fields. For example, medical image processing can help doctors diagnose diseases more accurately, and video surveillance systems can improve the accuracy and speed of image recognition. Image processing technology based on artificial intelligence and machine learning has achieved many breakthroughs and innovations with broad application prospects and commercial value. The technology will play an increasingly important role in image processing, helping people to understand better and apply image data and promoting the development and application of artificial intelligence and machine learning technologies.
