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Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators.

Bai X - Sensors (Basel) (2015)

Bottom Line: Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed.Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features.All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

View Article: PubMed Central - PubMed

Affiliation: Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing 100191, China. jackybxz@buaa.edu.cn.

ABSTRACT
The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

No MeSH data available.


Related in: MedlinePlus

Quantitative comparison using measure entropy.
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sensors-15-17149-f004: Quantitative comparison using measure entropy.

Mentions: Infrared and visual images obtained under different environments are processed by different algorithms. The mean value of the entropy, spatial frequency, mean gradient and Q measure values of all the fusion results related to each algorithm is shown in Figure 4, Figure 5, Figure 6 and Figure 7, respectively.


Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators.

Bai X - Sensors (Basel) (2015)

Quantitative comparison using measure entropy.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541927&req=5

sensors-15-17149-f004: Quantitative comparison using measure entropy.
Mentions: Infrared and visual images obtained under different environments are processed by different algorithms. The mean value of the entropy, spatial frequency, mean gradient and Q measure values of all the fusion results related to each algorithm is shown in Figure 4, Figure 5, Figure 6 and Figure 7, respectively.

Bottom Line: Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed.Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features.All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

View Article: PubMed Central - PubMed

Affiliation: Image Processing Center, Beijing University of Aeronautics and Astronautics, Beijing 100191, China. jackybxz@buaa.edu.cn.

ABSTRACT
The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

No MeSH data available.


Related in: MedlinePlus