BEIJING, March 27, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced the development of a convolutional neural network-based image fusion algorithm system. The application of convolutional neural networks to image fusion has obvious advantages: it can improve the feature extraction and assignment aspects of image fusion and enhance the quality of fused images.
Image fusion is the processing and fusion of two or more images acquired by different sensors. Image fusion can achieve complementary information between images and maximize image quality to generate content-rich and more visually perceptive fused images and then complete the analysis and processing of information. CNN is a typical deep-learning model. It learns feature-representation mechanisms at different levels of abstraction from signal or image data. CNN extracts features of the input image by learning filters to obtain other feature maps at each level, and each unit or coefficient in the feature map is called a neuron. Different computational methods, convolution, activation function, and pooling, are generally used to connect the feature maps between adjacent levels.
The key advantage of WiMi's CNN-based image fusion algorithm is that it maximizes the extraction of useful information from the source image and fuses it into the resultant image to obtain a high-quality image.
The system first acquires the images to be fused and preprocesses them. Then the preprocessed images are input to the convolutional neural network for training. The system extracts its image fusion features and then uses the optimal thresholding method to segment the fusion features and fuse different regions of different images accordingly to get the final image fusion results.
A complete CNN is a multi-layer structure that includes an input layer, a pooling layer, a fully connected layer, and a convolutional layer. The convolutional layer is the most critical part, which contains multiple neural network nodes to extract the features for image fusion. The pooling layer can downscale the image fusion features, obtain a new image fusion feature mapping set, and then iterate continuously through the weights for training and learning. Before performing optimal image fusion, the system will segment the image into different regions. And the image to be fused is divided into different regions by the optimal segmentation threshold, and the different regions are fused to output the image fusion result.
This system's processed images have significantly higher clarity and brightness, improved image signal-to-noise ratio, and higher image quality, which can obtain better image visual effects. The system has obvious advantages over traditional image fusion technology.
CNN-based image fusion algorithm has become a fundamental image analysis and computer vision technology. With the continuous improvement of hardware level and related research, its application in various fields will continue to develop in depth. WiMi's algorithm has a wide range of application prospects in the target recognition, intelligent robotics, medical image processing, industrial Internet, etc.
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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