Fusion of Infrared and Visible Images Based on Non-subsampled Contourlet Transform and Intuitionistic Fuzzy Set

Huaiyu, 蔡怀宇 Cai; Liran, 卓励然 Zhuo; Pan, 朱攀 Zhu; Zhanhua, 黄战华 Huang; Xiaoyu, 武晓宇 Wu

doi:10.3788/gzxb20184706.0610002

Cited by 7 publications

(3 citation statements)

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“…Gauss membership function is used to represent the membership degree of the coefficients, and the final low-frequency image is fused in accordance with the membership degree after defuzzification. The membership

and non-membership

are respectively shown as follows [ 33 ]:

where

represents the average value of

represents the standard deviation.

and

are Gaussian function adjustment parameters.…”

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

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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 non-membership

are respectively shown as follows [ 33 ]:

where

represents the average value of

represents the standard deviation.

and

are Gaussian function adjustment parameters.…”

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

“…Based on the different feature extraction and fusion strategies, these methods can be classified into conventional fusion methods and end-to-end deep learning methods. According to the hand-crafted feature decomposition and generation rules, conventional fusion methods mainly consist of multiscale transform-based [10], sparse representation-based [11][12][13], saliency-based [14][15][16][17], fuzzy set-based [18][19][20], and hybridbased [21][22][23] methods. To summarize, conventional methods for image fusion typically comprise three primary stages.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Infrared and Visible Image Fusion via Adaptive DenseNet with Knowledge Distillation

Zhao

Juanli

et al. 2023

Electronics

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The fusion of infrared and visible images produces a complementary image that captures both infrared radiation information and visible texture structure details using the respective sensors. However, the current deep-learning-based fusion approaches mainly tend to prioritize visual quality and statistical metrics, leading to an increased model complexity and weight parameter sizes. To address these challenges, we propose a novel dual-light fusion approach using adaptive DenseNet with knowledge distillation to learn and compress from pre-existing fusion models, which achieves the goals of model compression through the use of hyperparameters such as the width and depth of the model network. The effectiveness of our proposed approach is evaluated on a new dataset comprising three public datasets (MSRS, M3FD, and LLVIP), and both qualitative and quantitative experimental results show that the distillated adaptive DenseNet model effectively matches the original fusion models’ performance with smaller model weight parameters and shorter inference times.

show abstract

“…The abovementioned methods pay little attention to a phenomenon: pixel intensities are the feature in infrared thermal radiation messages, while edges and gradients are the feature in textural detail information in visible images. Due to two different characteristics of the image, edge blur and texture blur [27], [28] will occur. To address the abovementioned problem and get clearer fused image, we presented a new blur suppression generative adversarial network architecture.…”

Section: Introductionmentioning

confidence: 99%

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

Shi

Liu

et al. 2023

Chinese J. Elect.

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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

Fusion of Infrared and Visible Images Based on Non-subsampled Contourlet Transform and Intuitionistic Fuzzy Set

Cited by 7 publications

References 0 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

Lightweight Infrared and Visible Image Fusion via Adaptive DenseNet with Knowledge Distillation

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

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