LPGAN: A LBP-Based Proportional Input Generative Adversarial Network for Image Fusion
Abstract
:1. Introduction
- The extraction of source image feature information is incomplete. Most image fusion algorithms cannot achieve good structural similarity while retaining richly detailed features at the same time due to the incomplete extraction of feature information [26]. Specifically, when the algorithm performs better on SSIM and PSNR metrics, its performance on SD, AG, and SF metrics will be worse, such as DenseFuse [27]; the reverse is also true, such as in the cases of FusionGAN [28] and DDcGAN [29].
- The mission objectives and network structure do not match. The same network is employed to extract features while ignoring the feature distribution characteristics of different modal images, resulting in the loss of meaningful information. Infrared images and visible images have different imaging characteristics and mechanisms, and using the same feature extraction network cannot fully extract the features of different modal images, such as DenseFuse and U2Fusion [30].
- Improper loss function leads to missing features. In the previous methods, only the gradient is used as a loss to supervise the extraction of detailed features, while neglecting the extraction of lower level texture features in the source images. This makes it difficult for the network to fully utilize the feature information in the source images during the fusion process, such as FusionGAN and DDcGAN.
- (1)
- We introduce LBP into the network for the first time and design a new loss for the generator, which enables the model to make full use of different types of features in a balanced way and reduce image distortion.
- (2)
- We design a pseudo-Siamese network to extract feature information from source images. It fully considers the differences in the imaging mechanism and image features of the different source images, encouraging the generator to preserve more features in source images.
- (3)
- We propose a high-performance image fusion method (LPGAN), achieving the state-of-the-art on the TNO dataset and CVC14 dataset.
2. Related Work
2.1. Deep Learning-Based Image Fusion
- Due to the lack of ground-truth, the existing methods usually supervise the work of the model by adopting no-reference metrics as the loss function. However, only the gradient is used as the loss to supervise the extraction of the detailed features, and the texture information is always ignored.
- They ignore the information distribution of the source images, i.e., the visible image has more detailed information and the infrared image has more contrast information.
- These methods all use only one network to extract features from infrared images and visible images, ignoring the difference in imaging mechanisms between these two kinds of images.
2.2. Generative Adversarial Networks
2.3. Local Binary Patterns
3. Proposed Method
3.1. Overall Framework
3.2. Network Architecture
3.2.1. Generator Architecture
3.2.2. Discriminator Architecture
3.3. Loss Function
3.3.1. Loss Function of Generator
3.3.2. Loss Function of Discriminators
4. Experiments
4.1. Implementation
4.1.1. Dataset
4.1.2. Training Details
4.1.3. Metrics
4.2. Results on the TNO Dataset
4.2.1. Qualitative Comparison
4.2.2. Quantitative Comparison
4.3. Results on the CVC14 Dataset
4.3.1. Qualitative Comparison
4.3.2. Quantitative Comparison
4.4. Ablation Study
4.4.1. The Effect of LBP
4.4.2. The Effect of Proportional Input
4.5. Additional Results for RGB Images and Infrared Images
4.6. Additional Results for PET Images and MRI Images
4.7. Multi-Spectral Image Fusion Expansion Experiment
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Algorithms | SD | AG | SF | MI | EN | PSNR | SSIM | VIF |
---|---|---|---|---|---|---|---|---|
1:2 w/o LBP | 34.6916 | 7.2434 | 13.0421 | 1.6990 | 7.0477 | 14.4241 | 0.6287 | 0.8831 |
1:2 w/ LBP | 47.8106 | 7.7136 | 14.1130 | 2.1066 | 7.3885 | 14.0895 | 0.6167 | 0.8744 |
1:3 w/o LBP | 36.5414 | 7.7551 | 13.9280 | 1.7276 | 7.0850 | 13.8040 | 0.5578 | 0.8758 |
1:3 w/ LBP | 37.1527 | 8.4237 | 15.2863 | 1.7129 | 7.1475 | 14.4447 | 0.6134 | 0.8834 |
1:4 w/o LBP | 44.6710 | 7.5059 | 13.7169 | 1.9475 | 7.3457 | 14.4303 | 0.6035 | 0.8785 |
1:4 w/ LBP | 48.6349 | 7.7745 | 14.3483 | 2.2591 | 7.4292 | 14.1420 | 0.6213 | 0.8777 |
Algorithms | SD | AG | SF | EN | MI | PSNR | SSIM | CC |
---|---|---|---|---|---|---|---|---|
FusionGAN | 30.2032 | 5.3697 | 11.0368 | 6.4712 | 2.2562 | 15.3458 | 0.6251 | 0.6553 |
DenseFuse | 40.3449 | 8.2281 | 16.5689 | 6.8515 | 2.5893 | 16.3808 | 0.6899 | 0.7651 |
U2Fusion | 43.3423 | 10.7603 | 21.3030 | 6.9693 | 2.3393 | 16.5555 | 0.6664 | 0.7496 |
DDcGAN | 52.1831 | 10.8181 | 21.0603 | 7.4602 | 2.1890 | 14.2005 | 0.5887 | 0.6688 |
Ours | 43.3589 | 11.4636 | 22.6095 | 7.1701 | 2.4498 | 15.8229 | 0.6741 | 0.7499 |
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Yang, D.; Zheng, Y.; Xu, W.; Sun, P.; Zhu, D. LPGAN: A LBP-Based Proportional Input Generative Adversarial Network for Image Fusion. Remote Sens. 2023, 15, 2440. https://doi.org/10.3390/rs15092440
Yang D, Zheng Y, Xu W, Sun P, Zhu D. LPGAN: A LBP-Based Proportional Input Generative Adversarial Network for Image Fusion. Remote Sensing. 2023; 15(9):2440. https://doi.org/10.3390/rs15092440
Chicago/Turabian StyleYang, Dongxu, Yongbin Zheng, Wanying Xu, Peng Sun, and Di Zhu. 2023. "LPGAN: A LBP-Based Proportional Input Generative Adversarial Network for Image Fusion" Remote Sensing 15, no. 9: 2440. https://doi.org/10.3390/rs15092440
APA StyleYang, D., Zheng, Y., Xu, W., Sun, P., & Zhu, D. (2023). LPGAN: A LBP-Based Proportional Input Generative Adversarial Network for Image Fusion. Remote Sensing, 15(9), 2440. https://doi.org/10.3390/rs15092440