Abstract
The article provides an infrared and visible image fusion algorithm based on directionlets transform. The registered original images were decomposed into the low-frequency and high-frequency coefficients by directionlets transform. Getting the mean of the low-frequency coefficients, applying the local variance maximum principle to the high-frequency coefficients, thereby the fusion coefficients of the fused image can be acquired. Finally, the fused image was obtained using inverse directionlets transform. The experiment indicates that the fusion algorithm can extract the original image features better. Compared with the traditional fusion algorithms, the fusion algorithm presented in this article provides better subjective visual effect, and the standard deviation and entropy value would be somewhat increased.
Highlights
Infrared and visible image fusion is part of multi-source image fusion
Multi-source image fusion is the process of acquiring unified description towards the formation of high-performance perception system with employing different types of image sensors, and combing two of more kinds of image information effectively
Image fusion is mainly divided into three levels, namely the pixel-level fusion, feature fusion, and decision-level
Summary
Infrared and visible image fusion is part of multi-source image fusion. Multi-source image fusion is the process of acquiring unified description towards the formation of high-performance perception system with employing different types of image sensors, and combing two of more kinds of image information effectively. There are many existing pixel-level image fusion methods, including the weighted average method [2], the tower decomposition method [3], principal component analysis (PCA) method [4] as well as fusion based on wavelet transform methods. The wavelet transform has become an important tool in image fusion method for its excellent feature of time frequency analysis [5].
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