Research Topic

Recent Advances in Artificial Neural Networks and Embedded Systems for Multi-Source Image Fusion

About this Research Topic

Multi-source visual information fusion can help the robotic system to perceive the real world, and image fusion is a computational technique fusing the multi-source images from multiple sensors into a synthesized image that provides either comprehensive or reliable description. At present, a lot of brain-inspired algorithms methods (or models) are aggressively proposed to accomplish this task, and the artificial neural network has become one of the most popular techniques in processing multi-source image fusion in this decade, especially deep convolutional neural networks. This is an exciting research field for the research community of image fusion and there are many interesting issues that remain to be explored, such as deep few-shot learning, unsupervised learning, application of embodied neural systems, and industrial applications.

How to develop a sound biological neural network and embedded system to fuse the multiple features of source images are basically two key questions that need to be addressed in the field of multi-source image fusion. Hence, studies of image fusion can be divided into two aspects: first, new end-to-end neural network models for merge constituent parts during the image fusion process; Second, the embodiment of artificial neural networks for image fusion systems. In addition, current booming techniques, including deep neural systems and embodied artificial intelligence systems, are considered as potential future trends for reinforcing the performance of image fusion.

This Research Topic focuses on the new ideas, models, and methods in artificial neural networks and embedded systems for multi-source image fusion. We welcome all Specialty Grand Challenge, Perspective, Brief Research Report, Original Research Articles, and Reviews. Themes to be investigated may include, but are not limited to:

Neural Network Models and Techniques:
-Deep Convolutional Neural Networks for Image Fusion
-Generative Adversarial Networks for Image Fusion
-Neurodynamic Analysis for Image Fusion
-Learning Systems for Image Fusion
-Fuzzy Neural Networks for Image Fusion
-Bionic Image Fusion for Robotic System

Feature Extraction and Fusion Strategies:
-Image Feature Extraction based on Deep Neural Networks
-Intelligent Sensing-based Decision Support Systems for Image Fusion
-Feature Presentation Methods for Image Fusion
-Fused Image Quality Assessment
-Image fusion Strategies on Neural Networks
-Adaptive Image Fusion Strategies for Robotic System

Techniques on Real-World Applications:
-Industrial Applications of Image Fusion
-Embedded Learning System for Image Fusion
-Real-Time Image Fusion System
-System on Chip for Image Fusion
-Model Acceleration for Image Fusion
-Lightweight Image Fusion Techniques for Robotic System


Keywords: Artificial Neural Networks, Embedded Learning System, Feature Extraction, Fusion Strategy, Image Fusion


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Multi-source visual information fusion can help the robotic system to perceive the real world, and image fusion is a computational technique fusing the multi-source images from multiple sensors into a synthesized image that provides either comprehensive or reliable description. At present, a lot of brain-inspired algorithms methods (or models) are aggressively proposed to accomplish this task, and the artificial neural network has become one of the most popular techniques in processing multi-source image fusion in this decade, especially deep convolutional neural networks. This is an exciting research field for the research community of image fusion and there are many interesting issues that remain to be explored, such as deep few-shot learning, unsupervised learning, application of embodied neural systems, and industrial applications.

How to develop a sound biological neural network and embedded system to fuse the multiple features of source images are basically two key questions that need to be addressed in the field of multi-source image fusion. Hence, studies of image fusion can be divided into two aspects: first, new end-to-end neural network models for merge constituent parts during the image fusion process; Second, the embodiment of artificial neural networks for image fusion systems. In addition, current booming techniques, including deep neural systems and embodied artificial intelligence systems, are considered as potential future trends for reinforcing the performance of image fusion.

This Research Topic focuses on the new ideas, models, and methods in artificial neural networks and embedded systems for multi-source image fusion. We welcome all Specialty Grand Challenge, Perspective, Brief Research Report, Original Research Articles, and Reviews. Themes to be investigated may include, but are not limited to:

Neural Network Models and Techniques:
-Deep Convolutional Neural Networks for Image Fusion
-Generative Adversarial Networks for Image Fusion
-Neurodynamic Analysis for Image Fusion
-Learning Systems for Image Fusion
-Fuzzy Neural Networks for Image Fusion
-Bionic Image Fusion for Robotic System

Feature Extraction and Fusion Strategies:
-Image Feature Extraction based on Deep Neural Networks
-Intelligent Sensing-based Decision Support Systems for Image Fusion
-Feature Presentation Methods for Image Fusion
-Fused Image Quality Assessment
-Image fusion Strategies on Neural Networks
-Adaptive Image Fusion Strategies for Robotic System

Techniques on Real-World Applications:
-Industrial Applications of Image Fusion
-Embedded Learning System for Image Fusion
-Real-Time Image Fusion System
-System on Chip for Image Fusion
-Model Acceleration for Image Fusion
-Lightweight Image Fusion Techniques for Robotic System


Keywords: Artificial Neural Networks, Embedded Learning System, Feature Extraction, Fusion Strategy, Image Fusion


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

31 August 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

31 August 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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