Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition
In Multi-scale Geometric Analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of the 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 into 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.
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Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Posted 14 May, 2020
On 24 Aug, 2020
On 02 Aug, 2020
Received 20 May, 2020
On 18 May, 2020
Received 17 May, 2020
On 15 May, 2020
Invitations sent on 14 May, 2020
On 30 Apr, 2020
On 29 Apr, 2020
On 29 Apr, 2020
On 21 Apr, 2020
Received 19 Apr, 2020
On 10 Mar, 2020
On 08 Mar, 2020
Received 08 Mar, 2020
Invitations sent on 07 Mar, 2020
On 27 Feb, 2020
On 26 Feb, 2020
On 26 Feb, 2020
On 20 Feb, 2020
Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition
Posted 14 May, 2020
On 24 Aug, 2020
On 02 Aug, 2020
Received 20 May, 2020
On 18 May, 2020
Received 17 May, 2020
On 15 May, 2020
Invitations sent on 14 May, 2020
On 30 Apr, 2020
On 29 Apr, 2020
On 29 Apr, 2020
On 21 Apr, 2020
Received 19 Apr, 2020
On 10 Mar, 2020
On 08 Mar, 2020
Received 08 Mar, 2020
Invitations sent on 07 Mar, 2020
On 27 Feb, 2020
On 26 Feb, 2020
On 26 Feb, 2020
On 20 Feb, 2020
In Multi-scale Geometric Analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of the 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 into 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.