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The use of thermal imaging is a benefit for the Armed Forces. Due to their advantages, they have a large
number of applications, including the detection of camouflaged people. For better results, the thermal information can be merged with the color information which allows a greater detail, resulting in a greater degree of security.
The present study implemented as pixel level image fusion methods: Principal Components Analysis; Laplacian Pyramid; and Discrete Wavelet Transform.
A qualitative analysis concluded that the method which performs better is the one that uses Wavelets, followed by the Laplacian Pyramid and finally the PCA. A quantitative analysis was made using as performance metrics: Standard Deviation, Entropy, Spatial Frequency, Mutual Information, Fusion
Quality Index and Structural Similarity Index. The values obtained support the conclusions drawn from the qualitative analysis. The Mutual Information, Fusion Quality Index and Structural Similarity Index are the appropriate metrics to measure the quality of image fusion as they take into account the
relationship between the fused image and the input images.
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Laplacian Pyramid . Pixel level image fusion Performance metrics Principal component analysis Security Wavelets