Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing

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Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing
by Yun Zhang, Sam Kwong
English | 2024 | ISBN: 3725817812 | 254 Pages | PDF | 47 MB

This Topical Collection focuses on fundamental and applied research on deep learning based visual sensing, imaging and video processing. Deep learning techniques have shown their capabilities to discover knowledge from massive unstructured data, providing data-driven solutions for representation and decision making. They have demonstrated significant technical advancement potential for many research fields and applications, such as sensors and imaging, audio-visual signal processing, and pattern recognition. Today, with the rapid advancements of advanced deep learning models, such as convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), generative adversarial network (GAN), and transformer network, learning techniques, such as transfer learning, reinforcement learning, federal learning, multi-task learning, and meta-learning, and the increasing demands around effective visual signal processing, new opportunities are emerging in deep-learning-based sensing, imaging, and video processing.

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