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

Yun Zhang

ISBN 10: 3725817812 / ISBN 13: 9783725817818

Published by Mdpi AG, 2024

About this Item

Hardcover. deep learning; neural network; image enhancement; visual quality assessment; image classification; visual understandingThis 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783725817818

Bibliographic Details

• Title: Advances in Deep-Learning-Based Sensing, Imaging, and Video Processing (Hardcover)
• Publisher: Mdpi AG
• Publication Date: 2024
• Language: English
• ISBN 10: 3725817812
• ISBN 13: 9783725817818
• Binding: Hardcover
• Condition: new

About this title

Synopsis

deep learning; neural network; image enhancement; visual quality assessment; image classification; visual understandingThis 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.