Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study

Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions fo...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2012-01, Vol.34 (1), p.94-109
Main Authors: Liu, Z., Blasch, E., Xue, Z., Zhao, J., Laganiere, R., Wu, W.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Artificial intelligence
Assessments
Computer science
control theory
systems
context enhancement
Distortion
Exact sciences and technology
Image enhancement
Image fusion
Image processing
image quality
Image resolution
Measurement
Memory and file management (including protection and security)
Memory organisation. Data processing
multiresolution analysis
Night vision
objective fusion assessment
Pattern analysis
Pattern recognition. Digital image processing. Computational geometry
performance metric
Pixel
pixel-level image fusion
Software
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Summary:Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2011.109