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Infrared and visible image fusion based on multi‐channel convolutional neural network
2022
IET Image Processing
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For the lack of labels in infrared and visible image fusion network, an infrared and visible image fusion model based on multi-channel unsupervised convolutional neural network (CNN) is proposed in this ...
In contrast to conventional unsupervised fusion network, the proposed network contains three channels for extracting infrared features, visible features and common features of infrared and visible images ...
fusion model is established based on convolutional neural network. ...
doi:10.1049/ipr2.12431
fatcat:gy2hcefei5crjkiltzlmckjxqy
An Image Fusion Method Based on Special Residual Network and Efficient Channel Attention
2022
Electronics
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This paper presents an image fusion network based on a special residual network and attention mechanism. ...
Compared with the traditional fusion network, the image fusion network has the advantages of an end-to-end network and integrates the feature extraction advantages of the attention mechanism residual network ...
neural network based IFCNN is a general image fusion algorithm based on convolutional neural network, which is a more basic fusion method, only extracting the features of the source image through two ...
doi:10.3390/electronics11193140
fatcat:cbevoji5ujd4rjzkgozjm2lofe
Lightweight Cross-Modal Multispectral Pedestrian Detection Based on Spatial Reweighted Attention Mechanism
2024
Computers Materials & Continua
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This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model's focus on different spatial positions and sharing the weighted feature ...
We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model's detection efficiency. ...
Initially, the network captures the illumination values of both infrared and visible light images simultaneously using a miniature neural network. ...
doi:10.32604/cmc.2024.048200
fatcat:ihgdtdtdkzemjdd4slgb7b6chy
A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics
[article]
2020
arXiv
pre-print
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Firstly, we combine channel attention model with nonlinear convolutional neural network to select features and fuse nonlinear features. ...
Finally, in order to verify the superiority of our algorithm, we carried out experiments on the combined vision system image data set, and extended our algorithm to the infrared and visible image and the ...
Based on this problem and combined with the strong nonlinear fitting ability of the deep convolution neural network, we construct the deep convolution neural network with the characteristics of feature ...
arXiv:1912.08577v4
fatcat:lajicugsc5cllpjzihqag6tcda
Explicit and implicit models in infrared and visible image fusion
[article]
2022
arXiv
pre-print
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Infrared and visible images, as multi-modal image pairs, show significant differences in the expression of the same scene. ...
Aiming at the advantages and limitations to be solved by existing algorithms, we discuss the main problems of multi-modal image fusion and future research directions. ...
Explicit Models Various encoder-decoder networks based on deep convolutional neural networks have been applied to infrared and visible image fusion in recent years. ...
arXiv:2206.09581v1
fatcat:zwef66oxprc3vhw76ovchzzx4a
TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network
[article]
2022
arXiv
pre-print
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In this paper, therefore, we propose an infrared and visible image fusion algorithm based on a lightweight transformer module and adversarial learning. ...
The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. ...
Image Fusion Method Based on Deep Learning The fusion algorithm based on deep learning has shown excellent performance in infrared and visible image fusion, multi-focus image fusion and medical image fusion ...
arXiv:2201.10147v2
fatcat:qn3q3dkwhnarnekmgye35tapre
Ship Classification Based on Attention Mechanism and Multi-Scale Convolutional Neural Network for Visible and Infrared Images
2020
Electronics
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This study proposes a ship classification method based on an attention mechanism and multi-scale convolutional neural network (MSCNN) for visible and infrared images. ...
First, the features of visible and infrared images are extracted by a two-stream symmetric multi-scale convolutional neural network module, and then concatenated to make full use of the complementary features ...
Acknowledgments: The authors would like to thank the anonymous reviewers for their very competent comments and helpful suggestions. ...
doi:10.3390/electronics9122022
fatcat:yao6zmwypbhzbko34zhzej6h5u
Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion
[article]
2022
arXiv
pre-print
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We propose a new image decomposition method based on convolutional neural network. This method can be applied to many image processing tasks. ...
We input infrared image and visible light image and decompose them into three high-frequency feature images and a low-frequency feature image respectively. ...
In 2017, Liu et al. proposed a method based on convolutional neural network for multi-focus image fusion [11] . In ICCV2017, Prabhakar et al. ...
arXiv:2102.10526v2
fatcat:2bppajynabapnj6s6qnnxdtshe
A Multi-Branch Multi-Scale Deep Learning Image Fusion Algorithm Based on DenseNet
2022
Applied Sciences
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This paper presents an infrared image and visual image fusion algorithm based on deep learning. ...
This paper tests infrared visual images, multi-focus images, and other data sets. The traditional image fusion algorithm is compared several with the current advanced image fusion algorithm. ...
image fusion algorithm based on self coding network and named it DenseFuse [12, 13] . ...
doi:10.3390/app122110989
fatcat:mewp6qm6onb7lj667fle4etkrm
A Two-To-One Deep Learning General Framework for Image Fusion
2022
Frontiers in Bioengineering and Biotechnology
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In reaction to this problem, this paper proposes a general image fusion framework based on an improved convolutional neural network. ...
and objective evaluation, while the average running time is at least 94% faster than the reference algorithm based on neural network. ...
In recent years, image fusion methods based on neural networks have been rapidly growing (Liu et al., 2018) . ...
doi:10.3389/fbioe.2022.923364
pmid:35979172
pmcid:PMC9376963
fatcat:qxk5vc7mvfevzg6lgzy7wtcova
Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
2020
Applied Sciences
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In this paper, we propose a novel method for infrared and visible image fusion with a deep learning framework based on a generative adversarial network (GAN) and a residual network (ResNet). ...
Infrared and visible image fusion can obtain combined images with salient hidden objectives and abundant visible details simultaneously. ...
In the article, the authors proposed a CNN based siamese network for multi-focus image fusion and extended it for infrared and visible image fusion. ...
doi:10.3390/app10020554
fatcat:4haucufiujafrgyxc2kxolhvg4
Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion
2021
EAI Endorsed Transactions on Scalable Information Systems
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In this paper, we propose a double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion. ...
Therefore, the fusion of infrared and visible images will be beneficial to the combination of infrared image's better target indication characteristics and visible image's scene clearing information. ...
This paper was funded by "Science and Technology project of Henan Province: Design of industrial pollution gas emission monitoring system based on Internet+, Project no. 18210221037." ...
doi:10.4108/eai.22-11-2021.172216
fatcat:dg7ohjqz5nbk3ob5vmlbbsyg7y
Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models
[article]
2023
arXiv
pre-print
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However, the existing infrared and visible image fusion methods rarely explore how to handle multi-spectral/channel data directly and achieve high color fidelity. ...
Then, we use the the denoising network to extract the multi-channel diffusion features with both visible and infrared information. ...
Generally, the current mainstream deep fusion methods can be divided into three categories: methods based on autoencoder (AE) [18] , methods based on convolutional neural network (CNN) [4] , [19] and ...
arXiv:2301.08072v1
fatcat:gnh6tawy7zbndhxzv5tbniu6ve
Infrared and visible image fusion using a shallow CNN and structural similarity constraint
2020
IET Image Processing
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In recent years, image fusion methods based on deep networks have been proposed to combine infrared and visible images for achieving better fusion image. ...
To address these problems, we propose an end-to-end shallow convolutional neural network with structural constraints, which has only one convolutional layer to fuse infrared and visible images. ...
based fusion methods fuse infrared and visible images with convolutional feature maps of pre-trained networks. ...
doi:10.1049/iet-ipr.2020.0360
fatcat:w25snv3lefavjdgex5p7lhwhem
NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models
2020
IEEE Transactions on Instrumentation and Measurement
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2020 pre-print arXiv:2007.00328v1 fatcat:vfgruukekncsfmwa5rmzk2vtj4Other Versions
In this paper we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. ...
The nest connection-based network can preserve significant amounts of information from input data in a multi-scale perspective. ...
[25] also presented a fusion network based on CNN and multi-level features. In the infrared and visible image fusion field, Li et al. ...
doi:10.1109/tim.2020.3005230
fatcat:tw7vnw4t4babrdpylrndmnsxry
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