Infrared and visible image fusion based on target-enhanced multiscale transform decomposition

Chen, Jun; Li, Xuejiao; Luo, Lei; Mei, Xiaoguang; Ma, Jiayi

doi:10.1016/j.ins.2019.08.066

Cited by 279 publications

(90 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many fusion methods have been proposed for this purpose. The methods most commonly used for image fusion are multi-scale transforms [4] [44] and representation learning based methods [19] [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Kittler

2020

IEEE Trans. on Image Process.

364

149

View full text Add to dashboard Cite

Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent lowrank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We build a novel image fusion framework based on MDLatLRR which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The test data path is "/IV images/". For more results on additional test data (new test data containing 20 pairs of images), please refer to supplementary materials3 The deep features were extracted by 'Conv5' 4. The fusion strategy is 'addition' and λ = 1e2.…”

mentioning

confidence: 99%

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Kittler

2020

IEEE Trans. on Image Process.

364

149

View full text Add to dashboard Cite

show abstract

“…In the first group of simulation tests, we used the presented method to fuse five typical infrared and visible images in the TNO datasets, namely, "Men in front of house," "Bunker," "Sandpath," "Kaptein_1123," and "barbed_wire_2". In addition, six MGAbased methods are selected for comparison experiments, including WT [23], TEMST [35], NSST with weighted average [36], NSST with WT [37], NSCT with WT [38], and CURV with WT [39].…”

Section: Comparison With Mga-based Methodsmentioning

confidence: 99%

Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition

Xing

Cheng

Luo

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

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 highfrequency sub-bands. To highlight the thermal radiation target, we construct a nonlinear transform function to determine the fusion weight of low-frequency subbands, 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.

show abstract

“…MGA-based methods are selected for comparison experiments, including WT [24] , TEMST [36] , NSST with weighted average [37] , NSST with WT [38] , NSCT with WT [39] and CURV with WT [40] .…”

Section: Comparison With Mga-based Methodsmentioning

confidence: 99%

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

Xing

LIU

Luo

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Infrared and visible image fusion based on target-enhanced multiscale transform decomposition

Cited by 279 publications

References 44 publications

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition

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

Contact Info

Product

Resources

About