Neural Network Architectures for Real-Time Image Processing

Abstract

Real-time image processing has become a cornerstone of numerous applications, including autonomous vehicles, medical imaging, industrial inspection, and augmented reality. Neural networks, particularly deep learning architectures, have shown remarkable success in enhancing the efficiency and accuracy of image processing systems. This article provides an overview of neural network architectures specifically designed for real-time image processing tasks, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and recurrent neural networks (RNNs). The study examines the computational challenges associated with real-time processing, such as speed and memory efficiency, and explores strategies for optimizing these networks to meet the requirements of real-time applications.