Combining Regional Energy and Intuitionistic Fuzzy Sets for Infrared and Visible Image Fusion

To get more obvious target information and more texture features, a new fusion method for the infrared (IR) and visible (VIS) images combining regional energy (RE) and intuitionistic fuzzy sets (IFS) is proposed, and this method can be described by several steps as follows. Firstly, the IR and VIS images are decomposed into low- and high-frequency sub-bands by non-subsampled shearlet transform (NSST). Secondly, RE-based fusion rule is used to obtain the low-frequency pre-fusion image, which allows the important target information preserved in the resulting image. Based on the pre-fusion image, the IFS-based fusion rule is introduced to achieve the final low-frequency image, which enables more important texture information transferred to the resulting image. Thirdly, the ‘max-absolute’ fusion rule is adopted to fuse high-frequency sub-bands. Finally, the fused image is reconstructed by inverse NSST. The TNO and RoadScene datasets are used to evaluate the proposed method. The simulation results demonstrate that the fused images of the proposed method have more obvious targets, higher contrast, more plentiful detailed information, and local features. Qualitative and quantitative analysis results show that the presented method is superior to the other nine advanced fusion methods.

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Infrared and visible image fusion method based on compound decomposition and intuitionistic fuzzy set

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20213940930 ◽

2021 ◽

Vol 39 (4) ◽

pp. 930-936

Author(s):

Yahui Zhu ◽

Li Gao

Keyword(s):

Image Fusion ◽

High Frequency ◽

Intuitionistic Fuzzy Set ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Method ◽

Visible Image ◽

Edge Information ◽

Intuitionistic Fuzzy ◽

Image Fusion Method

To overcome the shortcomings of traditional image fusion algorithms based on multiscale transform, an infrared and visible image fusion method based on compound decomposition and intuitionistic fuzzy set is proposed. Firstly, the non-subsampled contour transform is used to decompose the source image into low-frequency coefficients and high-frequency coefficients. Then the potential low-rank representation model is used to decompose low-frequency coefficients into basic sub-bands and salient sub-bands, in which the visual saliency map is taken as weighted coefficient. The weighted summation of low-frequency basic sub-bands is used as the fusion rule. The maximum absolute value of low-frequency salient sub-bands is also used as the fusion rule. The two fusion rules are superimposed to obtain low-frequency fusion coefficients. The intuitionistic fuzzy entropy is used as the fusion rule to measure the texture information and edge information of high-frequency coefficients. Finally, the infrared visible fusion image is obtained with the non-subsampled contour inverse transform. The comparison results on the objective and subjective evaluation of several sets of fusion images show that our image fusion method can effectively keep edge information and rich information on source images, thus producing better visual quality and objective evaluation than other image fusion methods.

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Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

10.21203/rs.2.24788/v2 ◽

2020 ◽

Author(s):

Xiaoxue XING ◽

Cheng LIU ◽

Cong LUO ◽

Tingfa XU

Keyword(s):

Thermal Radiation ◽

High Frequency ◽

Geometric Analysis ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Algorithm ◽

Visible Image ◽

Fused Image ◽

Fusion Methods ◽

Visible Images

Abstract 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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Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

10.21203/rs.2.24788/v1 ◽

2020 ◽

Author(s):

Xiaoxue XING ◽

Cheng LIU ◽

Cong LUO ◽

Tingfa XU

Keyword(s):

Thermal Radiation ◽

High Frequency ◽

Geometric Analysis ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Algorithm ◽

Visible Image ◽

Fused Image ◽

Fusion Methods ◽

Visible Images

Abstract 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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An Image Fusion Algorithm Based on NSCT

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.427-429.1589 ◽

2013 ◽

Vol 427-429 ◽

pp. 1589-1592

Author(s):

Zhong Jie Xiao

Keyword(s):

Image Fusion ◽

High Frequency ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Method ◽

Fusion Algorithm ◽

Visible Image ◽

Weighted Fusion ◽

Scene Information ◽

Feature Information

The study proposed an improved NSCT fusion method based on the infrared and visible light images characteristics and fusion requirement. This paper improved the high-frequency coefficient and low-frequency coefficient fusion rules. The low-frequency sub-band images adopted the pixel feature energy weighted fusion rule. The high-frequency sub-band images adopted the neighborhood variance feature information fusion rule. The fusion experiment results show that this algorithm has good robustness. It could effectively extract edges and texture information. The fused images have abundance scene information and clear target. So this algorithm is an effective infrared and visible image fusion method.

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Fusion of Visible and Infrared Images Based on Non-Sampling Contourlet and Wavelet Transform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1523 ◽

2014 ◽

Vol 599-601 ◽

pp. 1523-1526

Author(s):

Yan Hai Wu ◽

Hao Zhang ◽

Fang Ni Zhang ◽

Yue Hua Han

Keyword(s):

Wavelet Transform ◽

High Frequency ◽

Infrared Image ◽

Low Frequency ◽

Infrared Images ◽

Fusion Image ◽

Visible Image ◽

Fusion Rules ◽

Visible Images

This paper gives a method for fusion of visible and infrared image, which combined non-sampling contourlet and wavelet transform. This method firstly makes contrast enhancements to infrared image. Next, does NSCT decomposition to visible image and enhanced-infrared image, then decomposes the low frequency from above decomposition using wavelet. Thirdly, for high-frequency subband of NSCT decomposition and high or low-frequency subband of wavelet, it uses different fusion rules. Finally, it gets fusion image through refactoring of wavelet and NSCT. Experiments show that the method not only retains texture details belong to visible images, but also highlights targets in infrared images. It has a better fusion effect.

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Infrared and visible image fusion method based on rolling guidance filter and NSST

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319500450 ◽

2019 ◽

Vol 17 (06) ◽

pp. 1950045 ◽

Cited By ~ 2

Author(s):

Cheng Zhao ◽

Yongdong Huang

Keyword(s):

Image Fusion ◽

High Frequency ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Method ◽

Visible Image ◽

Guided Filtering ◽

Gradient Domain ◽

Visible Images ◽

Image Fusion Method

The rolling guidance filtering (RGF) has a good characteristic which can smooth texture and preserve the edges, and non-subsampled shearlet transform (NSST) has the features of translation invariance and direction selection based on which a new infrared and visible image fusion method is proposed. Firstly, the rolling guidance filter is used to decompose infrared and visible images into the base and detail layers. Then, the NSST is utilized on the base layer to get the high-frequency coefficients and low-frequency coefficients. The fusion of low-frequency coefficients uses visual saliency map as a fusion rule, and the coefficients of the high-frequency subbands use gradient domain guided filtering (GDGF) and improved Laplacian sum to fuse coefficients. Finally, the fusion of the detail layers combines phase congruency and gradient domain guided filtering as the fusion rule. As a result, the proposed method can not only extract the infrared targets, but also fully preserves the background information of the visible images. Experimental results indicate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.

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A Multi-Focus Image Fusion Based on Wavelet and Block-Dividing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.719-720.988 ◽

2015 ◽

Vol 719-720 ◽

pp. 988-993

Author(s):

Hui Zhu Ma ◽

Qi Gui Nie

Keyword(s):

High Frequency ◽

Low Frequency ◽

Fusion Rule ◽

Fusion Method ◽

Fusion Image ◽

Clear Area ◽

Focus Image ◽

Simulation Results ◽

Ringing Effect ◽

Single Pixel

The traditional fusion rules of multi-focus image are largely centered on the fusion rule of high frequency coefficients, and those rules are all based on single pixel. Which leads to serious ringing effect, and reduces the visual effect of fusion image. The energy of an image is concentrated in the low frequency part after Wavelet Transform, and multi-focus image has the characteristic that the vast majority of adjacent pixels are either the clear area, or the blur area. Based on the above analysis, a new fusion method to multi-focus image is presented in this paper. The simulation results show that the proposed method is more feasible than common methods in processing multi-focus image.

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