Multi-Sensor Imaging and Fusion: Methods, Evaluations, and Applications, volume II

34.3K

views

87

authors

16

articles

Editorial

10 July 2024

Editorial: Multi-sensor imaging and fusion: methods, evaluations, and applications, volume II

Guanqiu Qi

,

Zhiqin Zhu

,

Yu Liu

,

Huafeng Li

, 1 more and

Jinxing Li

922 views

0 citations

Editors

6

Zhiqin Zhu

Chongqing University of Posts and Telecommunications

Guanqiu Qi

Buffalo State College

Yu Liu

Hefei University of Technology

Huafeng Li

Kunming University of Science and Technology

Bo Xiao

Imperial College London

Jinxing Li

Harbin Institute of Technology, Shenzhen

Impact

About

This Research Topic is Volume II of a series. The previous volume can be found here: Multi-Sensor Imaging and Fusion: Methods, Evaluations, and Applications

Multi-sensor image fusion focuses on processing images of the same object or scene acquired by multiple sensors, in which various sensors with multi-level and multi-spatial information are complemented and combined to ultimately yield a consistent interpretation of the observed environment. In recent years, multi-sensor image fusion has become a highly active topic, and various fusion methods have been proposed. Many effective processing methods, including multi-scale transformation, fuzzy inference, and deep learning, have been introduced to design fusion algorithms. Despite the great progress, there are still some noteworthy challenges in the field, such as the lack of unified fusion theories and methods for effective generalized fusion, the lack of fault tolerance and robustness, the lack of benchmarks for performance evaluation, the lack of work on specific applications of multi-sensor image fusion, and so on.

This research topic focuses on reporting advanced studies related to multi-sensor image fusion, including methods, evaluations, and applications, aiming to promote the development of multi-sensor image fusion techniques and applied it to medical image segmentation, biology analysis and astronomy imaging. More specifically, this research topic expects high-quality work such as unified fusion theories and effective generalized fusion methods, effective performance evaluation methods for multi-sensor image fusion, and practical applications of multi-sensor image fusion in classification, detection and segmentation tasks. Multi-sensor fusion methods can be developed based on traditional processing methods or deep learning methods. For performance evaluations, benchmarks involving datasets, objective metrics, and baseline creation methods are welcome to be investigated. In addition, specific applications are also welcome to be studied, such as the fusion of images from different modalities using multi-sensor fusion techniques.

• Multi-sensor image registration
• Multi-sensor image fusion
• High dynamic range imaging
• Multi-sensor/modality object detection and segmentation
• Machine learning/deep learning for multi-sensor image processing
• Multi-sensor image fusion datasets and benchmarks
• Objective evaluation of multi-sensor image fusion
• Multi-sensor image fusion in different fields
• Multi-sensor information monitoring and detection
• Multi-sensor information processing

Download ebook

PDF

EPUB

Share

Editors

Zhiqin Zhu

Chongqing University of Posts and Telecommunications

Guanqiu Qi

Buffalo State College

Yu Liu

Hefei University of Technology

Huafeng Li

Kunming University of Science and Technology

Bo Xiao

Imperial College London

Jinxing Li

Harbin Institute of Technology, Shenzhen

Impact

34,254 Total views

24,095 Article views

8,398 Article downloads

1,761 Topic views

Published In

Frontiers in Physics

Radiation Detectors and Imaging

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Suggest a topic