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Proceedings Paper

Multisensory data exploitation using advanced image fusion and adaptive colorization
Author(s): Yufeng Zheng; Kwabena Agyepong; Ognjen Kuljaca
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Paper Abstract

Multisensory data usually present complimentary information such as visual-band imagery and infrared imagery. There is strong evidence that the fused multisensor imagery increases the reliability of interpretation, and the colorized multisensor imagery improves observer performance and reaction times. In this paper, we propose an optimized joint approach of image fusion and colorization in order to synthesize and enhance multisensor imagery such that the resulting imagery can be automatically analyzed by computers (for target recognition) and easily interpreted by human users (for visual analysis). The proposed joint approach provides two sets of synthesized images, a fused image in grayscale and a colorized image in color using a fusion procedure and a colorization procedure, respectively. The proposed image fusion procedure is based on the advanced discrete wavelet (aDWT) transform. The fused image quality (IQ) can be further optimized with respect to an IQ metric by implementing an iterative aDWT procedure. On the other hand, the daylight coloring technique renders the multisensor imagery with natural colors, which human users are use to observing in everyday life. We hereby propose to locally colorize the multisensor imagery segment by mapping the color statistics of the multisensor imagery to that of the daylight images, with which the colorized images resemble daylight pictures. This local coloring procedure also involves histogram analysis, image segmentation, and pattern recognition. The joint fusion and colorization approach can be performed automatically and adaptively regardless of the image contents. Experimental results with multisensor imagery showed that the fused image is informative and clear, and the colored image appears realistic and natural. We anticipate that this optimized joint approach for multisensor imagery will help improve target recognition and visual analysis.

Paper Details

Date Published: 17 April 2008
PDF: 12 pages
Proc. SPIE 6968, Signal Processing, Sensor Fusion, and Target Recognition XVII, 69681U (17 April 2008); doi: 10.1117/12.784043
Show Author Affiliations
Yufeng Zheng, Alcorn State Univ. (United States)
Kwabena Agyepong, Alcorn State Univ. (United States)
Ognjen Kuljaca, Alcorn State Univ. (United States)


Published in SPIE Proceedings Vol. 6968:
Signal Processing, Sensor Fusion, and Target Recognition XVII
Ivan Kadar, Editor(s)

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