Abstract
Background: Image fusion is a fundamental issue in image processing and data fusion, as described in various patents. Its purpose is to synthesize important scene information from two or more images to a fusion one, which is more adaptive to human vision system or sub-sequential processing, such as image classification, object tracking and so on.
Method: In this paper, we proposed a novel image fusion method based on Shearlet Transformation, which is thought to be a suitable multi-scale geometric analysis tool to represent image. Firstly, we decomposed the source images into the shearlet domain. Secondly, we designed two kinds of measurements with local information in the same subband and the cousin coefficient in other direction subband respectively. Result: Then, we make a fusion criterion by incorporating the two measurements mentioned above. Last, the fusion image is obtained by inverse Shearlet Transfomation. Conclusion: The experimental results demonstrated that our proposed method outperformed the compared state-of-the-art fusion algorithms.Keywords: Image fusion, multi-scale geometric analysis, fusion rule, shearlet transformation, object tracking.
Related Books
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Recent Advances in IoT and Blockchain Technology
Augmented Intelligence: Deep Learning, Machine Learning, Cognitive Computing, Educational Data Mining
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments Part II
Machine Learning and Its Application: A Quick Guide for Beginners
Computational Intelligence For Data Analysis
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Advanced Computing Techniques: Implementation, Informatics and Emerging Technologies
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns
Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments (Part I)
7
2
1