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Long-Wave Infrared and Visible Image Fusion for Situational Awareness
Nathaniel Walker
Agenda
• What is image fusion?
• Applications
• System-level considerations
• Image fusion algorithms
• Image quality metrics
• Further research
What is Image Fusion?
• Combine data from multiple sensors into a single image• Visible
• Image Intensified (I2)
• Near Infrared (NIR)
• Short-wave Infrared (SWIR)
• Medium-wave Infrared (MWIR)
• Long-wave Infrared (LWIR)
• X-Ray
• Enhance the capabilities of the human visual system• ‘See’ outside the visible spectrum
• All-weather visibility
Applications
• Surveillance and Targeting
• Navigation
• Satellites
• Guidance/Detection Systems
System-Level Considerations
• Parallax• Optical alignment• Image registration• Sensor pixel resolution• Color vs. grayscale
• Spectral resolution can be lost in fusion
• Human factors• Presentation of IR data
• Realism of displayed data (superposition, contrast reversal)
• Preserving relative intensity across the scene
Image Fusion Algorithms (Zhang, Blum 1999)
• Weight-based combinations of the two sources• linear combination
• general loss of contrast
• Feature extraction• High-pass filtering or edge detection
• Maximizing image quality metrics
Image Quality Metrics
• Mostly done by subjective evaluation• ‘Optimal’ methods are task and application dependent
• Two classes of quantitative metrics (Chen, et al. 2005)• Analysis of the fused image
• standard deviation – measure of contrast
• entropy - measure of information content
• SNR
• Comparison of the fused image to the source images
• cross-entropy
• objective edge based measure
• universal index based measure
Further Research
• Concentration on grayscale fusion algorithms for effective communication of spectral information to the viewer• Sensor Assumptions
• perfect optical alignment and image registration
• same pixel resolution and field of view (FOV)
• Compare quantitative metrics of image quality to subjective image evaluation for situational awareness
• Focus on human factors for injecting infrared content into a visible spectrum image• What approach adds value without causing distraction or removing
detail
References
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