WiMi to Work on Convolutional Neural Network-Based Image Enhancement Algorithms

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider, announced that it is working on image enhancement algorithms based on CNN (convolutional neural network). CNNs have had significant achievements in many fields, such as computer vision and natural language processing. Applying convolutional neural networks to image enhancement has obvious advantages and can solve challenges in different environments.

The essence of CNN is to map the input image to a new mathematical model through multiple data transformations or dimensionality reduction. A CNN consists mainly of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer.

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The convolution layer performs a convolution operation on the input image or the output features of the previous layer, calculates the inner product of the entire convolution kernel and the corresponding position of the input image or feature map, and extracts the relevant image feature map. The pooling layer reduces the number of parameters and computational effort in the network by reducing the dimensionality of the activation feature map, maintaining the feature scale invariance property, and reducing overfitting to a certain extent. The pooling layer can downsample the image using the basis associated with the image section, reducing the amount of computational data and retaining valid information values. After multiple convolutional pooling operations on the image, the convolutional neural network classifies the features through the fully connected layer by using the one-dimensional activation feature vector obtained by expanding the three-dimensional activation feature map as input to the fully connected layer.

WiMi’s CNN-based image enhancement algorithms have substantial advantages in both extracting image feature information and feature representation. CNNs can share weights, perform convolutional calculations, and have powerful feature learning and mapping capabilities. It also ensures noise suppression and image detail preservation, has exceptionally high invariance during image displacement, scaling, and other deformations and exhibits better-reconstructed image quality.

CNNs can learn complex hierarchical features of images and accomplish complex image recognition tasks. At the same time, CNN-based feature extraction can understand a picture’s deep semantic feature information. This enables it to capture the contextual content of an image well and to train and learn the input image repeatedly, ultimately obtaining the best image enhancement effect to meet the requirements of the human visual system for images.

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Currently, image enhancement algorithms based on CNN are widely used in security, medicine, and ecology. In the era of rapid global information development, world knowledge is increasingly dependent on the explosive transmission of information. Most people still know the world mainly through their eyes. Therefore, images are not only a carrier of human visual information but also an essential medium for disseminating information. To obtain practical information from images quickly, the demand for image quality is increasing, the need for image enhancement will continue to grow, and the field of application of image enhancement technology will be further expanded.

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