Abstract
In multi-scale geometric analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and non-subsampled shearlet transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the wavelet transform (WT) is used to decompose high-frequency sub-bands to obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.
Highlights
Image fusion technology, which aims to combine images obtained from different sensors to create a single and rich fused image [1], has been widely used in medical imaging [2, 3], remote sensing [4,5,6], object recognition [7, 8], and detection [9]
We proposed a new fusion algorithm based on nonlinear enhancement and non-subsampled shearlet transform (NSST) decomposition for the infrared and visible images
The infrared and visible images to be fused are collected from TNO Image Fusion Dataset
Summary
Image fusion technology, which aims to combine images obtained from different sensors to create a single and rich fused image [1], has been widely used in medical imaging [2, 3], remote sensing [4,5,6], object recognition [7, 8], and detection [9]. Among the combination of different types of images, infrared and visible image fusion has attracted increasing attention [10]. The infrared images have less detail information, low contrast, poor visual effects, and poor imaging performance. The visible images can provide abundant detail information, while the target will be inconspicuous and influenced by smoke, bad weather conditions, and other factors. Fusion of the two types of the images can compensate for the insufficient imaging competence of infrared and visible sensors [11]. The final fused image can possess clearer scene information as well as better target characteristics [12]
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