Multi-modal Image Fusion - Techniques and Applications: Exploring multi-modal image fusion techniques for combining information from different imaging modalities for enhanced analysis

Authors

  • Dr. Olga Petrova Professor of Information Technology, Mälardalen University, Sweden Author

Keywords:

Multi-modal, Applications

Abstract

Multi-modal image fusion is a vital process in modern imaging, aiming to combine complementary information from different imaging modalities to enhance the analysis and interpretation of images. This paper presents an overview of various techniques and applications of multi-modal image fusion, highlighting its importance in medical imaging, remote sensing, and other fields. We discuss the challenges and recent advances in multi-modal image fusion, including deep learning-based approaches, and provide insights into future research directions.

Downloads

Download data is not yet available.

References

Prabhod, Kummaragunta Joel. "ANALYZING THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES IN IMPROVING PRODUCTION SYSTEMS." Science, Technology and Development 10.7 (2021): 698-707.

Sadhu, Amith Kumar Reddy, and Ashok Kumar Reddy Sadhu. "Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network." Journal of Science & Technology 1.1 (2020): 171-195.

Tatineni, Sumanth, and Karthik Allam. "Implementing AI-Enhanced Continuous Testing in DevOps Pipelines: Strategies for Automated Test Generation, Execution, and Analysis." Blockchain Technology and Distributed Systems 2.1 (2022): 46-81.

Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.

Perumalsamy, Jegatheeswari, Chandrashekar Althati, and Lavanya Shanmugam. "Advanced AI and Machine Learning Techniques for Predictive Analytics in Annuity Products: Enhancing Risk Assessment and Pricing Accuracy." Journal of Artificial Intelligence Research 2.2 (2022): 51-82.

Devan, Munivel, Lavanya Shanmugam, and Chandrashekar Althati. "Overcoming Data Migration Challenges to Cloud Using AI and Machine Learning: Techniques, Tools, and Best Practices." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 1-39.

Pelluru, Karthik. "Unveiling the Power of IT DataOps: Transforming Businesses across Industries." Innovative Computer Sciences Journal 8.1 (2022): 1-10.

Machireddy, Jeshwanth Reddy. "Data-Driven Insights: Analyzing the Effects of Underutilized HRAs and HSAs on Healthcare Spending and Insurance Efficiency." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 450-470.

Althati, Chandrashekar, Bhavani Krothapalli, and Bhargav Kumar Konidena. "Machine Learning Solutions for Data Migration to Cloud: Addressing Complexity, Security, and Performance." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 38-79.

Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "A Comparative Analysis of Lightweight Cryptographic Protocols for Enhanced Communication Security in Resource-Constrained Internet of Things (IoT) Environments." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 121-142.

Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.

Downloads

Published

03-03-2022

How to Cite

[1]
Dr. Olga Petrova, “Multi-modal Image Fusion - Techniques and Applications: Exploring multi-modal image fusion techniques for combining information from different imaging modalities for enhanced analysis”, Distrib Learn Broad Appl Sci Res, vol. 8, pp. 70–80, Mar. 2022, Accessed: Dec. 04, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/59

Similar Articles

41-50 of 114

You may also start an advanced similarity search for this article.