Multi-source visual information fusion and quality improvement 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, and quality improvement technique can be used to address the challenge of low-quality image analysis task. At present, a lot of brain-inspired algorithms methods (or models) are aggressively proposed to accomplish these two tasks, and the artificial neural network has become one of the most popular techniques in processing image fusion and quality improvement techniques 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 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 extract the multiple features of source images are basically two key questions that need to be addressed in the fields of image fusion and quality improvement. Hence, studies in this field can be divided into two aspects: first, new end-to-end neural network models for merging constituent parts during the image fusion process; Second, the embodiment of artificial neural networks for image processing 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 and quality improvement.
This Research Topic focuses on the new ideas, models, methods, and applications in artificial neural networks and embedded systems for image fusion and quality improvement. 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 and Quality Improvement
-Generative Adversarial Networks for Image Fusion and Quality Improvement
-Neurodynamic Analysis for Image Fusion and Quality Improvement
-Learning Systems for Image Fusion and Quality Improvement
-Fuzzy Neural Networks for Image Fusion and Quality Improvement
-Image Quality Assessment for Image Fusion and Quality Improvement
-Bionic Image Fusion and Quality Improvement for Robotic System
Feature Extraction and Fusion Strategies:
-Image Feature Extraction based on Deep Neural Networks
-Feature Extraction for low-quality image processing
-Intelligent Sensing-based Decision Support Systems for Image Fusion
-Feature Presentation Methods for Image Fusion and Quality Improvement
-Multilevel Feature Fusion for Image Fusion and Quality Improvement
-Image fusion Strategies on Neural Networks
-Adaptive Image Fusion Strategies for Robotic System
Techniques on Real-World Applications:
-Medical Robotics Vision for low-quality image
-Image Analysis Applications Using Image Fusion and Quality Improvement
-Embedded Learning System Using Image Fusion and Quality Improvement
-Real-Time System for Image Fusion and Quality Improvement
-System on Chip for Image Fusion and Quality Improvement
-Model Acceleration for Image Fusion and Quality Improvement
-Lightweight Image Fusion and Quality Improvement Techniques for Robotic System
Multi-source visual information fusion and quality improvement 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, and quality improvement technique can be used to address the challenge of low-quality image analysis task. At present, a lot of brain-inspired algorithms methods (or models) are aggressively proposed to accomplish these two tasks, and the artificial neural network has become one of the most popular techniques in processing image fusion and quality improvement techniques 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 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 extract the multiple features of source images are basically two key questions that need to be addressed in the fields of image fusion and quality improvement. Hence, studies in this field can be divided into two aspects: first, new end-to-end neural network models for merging constituent parts during the image fusion process; Second, the embodiment of artificial neural networks for image processing 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 and quality improvement.
This Research Topic focuses on the new ideas, models, methods, and applications in artificial neural networks and embedded systems for image fusion and quality improvement. 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 and Quality Improvement
-Generative Adversarial Networks for Image Fusion and Quality Improvement
-Neurodynamic Analysis for Image Fusion and Quality Improvement
-Learning Systems for Image Fusion and Quality Improvement
-Fuzzy Neural Networks for Image Fusion and Quality Improvement
-Image Quality Assessment for Image Fusion and Quality Improvement
-Bionic Image Fusion and Quality Improvement for Robotic System
Feature Extraction and Fusion Strategies:
-Image Feature Extraction based on Deep Neural Networks
-Feature Extraction for low-quality image processing
-Intelligent Sensing-based Decision Support Systems for Image Fusion
-Feature Presentation Methods for Image Fusion and Quality Improvement
-Multilevel Feature Fusion for Image Fusion and Quality Improvement
-Image fusion Strategies on Neural Networks
-Adaptive Image Fusion Strategies for Robotic System
Techniques on Real-World Applications:
-Medical Robotics Vision for low-quality image
-Image Analysis Applications Using Image Fusion and Quality Improvement
-Embedded Learning System Using Image Fusion and Quality Improvement
-Real-Time System for Image Fusion and Quality Improvement
-System on Chip for Image Fusion and Quality Improvement
-Model Acceleration for Image Fusion and Quality Improvement
-Lightweight Image Fusion and Quality Improvement Techniques for Robotic System
