Infrared and Visible Image Fusion Based on a Latent Low-Rank Representation Nested With Multiscale Geometric Transform

Yu, Shengsheng; Chen, Xiaopeng

doi:10.1109/access.2020.3001974

Cited by 21 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…

and

are firstly fused based on the RE-based fusion rule to achieve the pre-fused low-frequency image. The RE is calculated as follows [ 32 ]:

where

is the energy of the region centered on the point

, s represents IR or VIS;

is the neighborhood window centered on the point

;

is the low-frequency coefficient of the point

;

is the function value of the mask window of the point

. The window function W with a size of

can be expressed as Equation (10) [ 32 ]:

…”

Section: Proposed Methodsmentioning

confidence: 99%

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

Xing

Luo

Zhou

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…

and

are firstly fused based on the RE-based fusion rule to achieve the pre-fused low-frequency image. The RE is calculated as follows [ 32 ]:

where

is the energy of the region centered on the point

, s represents IR or VIS;

is the neighborhood window centered on the point

;

is the low-frequency coefficient of the point

;

is the function value of the mask window of the point

. The window function W with a size of

can be expressed as Equation (10) [ 32 ]:

…”

Section: Proposed Methodsmentioning

confidence: 99%

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

Xing

Luo

Zhou

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The fused image could contain balanced texture details and infrared intensity in this way. The loss function of dis-criminator is shown as (6).…”

Section: Contentmentioning

confidence: 99%

Infrared and Visible Image Fusion Based on Blur Suppression Generative Adversarial Network

Shi

Liu

et al. 2023

Chinese J. Elect.

View full text Add to dashboard Cite

The key to multi-sensor image fusion is the fusion of infrared and visible images. Fusion of infrared and visible images with generative adversarial network (GAN) has great advantages in automatic feature extraction and subjective vision improvement. Due to different principle between infrared and visible imaging, the blur phenomenon of edge and texture is caused in the fusion result of GAN. For this purpose, this paper conducts a novel generative adversarial network with blur suppression. Specifically, the generator uses the residual-in-residual dense block with switchable normalization layer as the elemental network block to retain the infrared intensity and the fused image textural details and avoid fusion artifacts. Furthermore, we design an anti-blur loss function based on Weber local descriptor. Finally, numerous experiments are performed qualitatively and quantitatively on public datasets. Results justify that the proposed method can be used to produce a fusion image with sharp edge and clear texture.

show abstract

“…Though the method is suitable for many image fusion applications, it requires high computational time. More works can be found in [40,45].…”

Section: Literature Reviewmentioning

confidence: 99%

QRCP Decomposition-Based Hybrid Approach for Fusion of Visible and Infrared Images

Rajakumar¹,

Satheeskumaran

2021

Circuits Syst Signal Process

View full text Add to dashboard Cite

Image fusion integrates several images from different modalities into a single image which contains more spatial and spectral resolution. Artifacts, smoothing and ringing are major issues in convolutional neural networks, edge-preserving filters and transform-based image fusion methods. Orthogonal rectangular with column pivoting (QRCP) matrix factorization-based hybrid approach is proposed in this work to overcome the above issues and to improve the fusion of visible and infrared images. QRCP decomposition is an accurate matrix decomposition that separates base layer and detail layer from source images. Discrete cosine transform and local spatial frequency concept are employed to fuse the base layers. Weight maps are utilized to transfer information into detail layers that are obtained directly from base layers. The obtained fused image is a linear combination of final base layer and final detail layer. The proposed method outperforms the existing methods in terms of performance measures such as entropy, spatial frequency, mutual information, normalized weight performance index, mean and standard deviation.

show abstract

Infrared and Visible Image Fusion Based on a Latent Low-Rank Representation Nested With Multiscale Geometric Transform

Cited by 21 publications

References 28 publications

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

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

Infrared and Visible Image Fusion Based on Blur Suppression Generative Adversarial Network

QRCP Decomposition-Based Hybrid Approach for Fusion of Visible and Infrared Images

Contact Info

Product

Resources

About