Automatic Machine Learning based Real Time Multi-Tasking Image Fusion
Pages 327 - 333
Abstract
Imaging systems work diversely in the image processing domain, and each system contains specific characteristics. We are developing models to fuse images from different sensors and environments to get promising outcomes for different computer vision applications. The multiple unified models have been developed for multiple tasks such as multi-focus (MF), multi-exposure (ME), and multi-modal (MM) image fusion. The careful tuning of such models is required to get optimal results, which are still not applicable to diverse applications. We propose an automatic machine learning (AML) based multi-tasking image fusion approach to overcome this problem. Initially, we evaluate source images with AML and feed them to the task-based models. Then, the source images are fused with the pre-trained and fine-tuned models. The experimental results authenticate the consequences of our proposed approach compared to generic approaches.
References
[1]
S. Karim, G. Tong, J. Li, A. Qadir, U. Farooq, and Y. Yu, “Current Advances and Future Perspectives of Image Fusion: A Comprehensive Review,” Inf. Fusion, 2022.
[2]
J. Du, W. Li, K. Lu, and B. Xiao, “An overview of multi-modal medical image fusion,” Neurocomputing, vol. 215, pp. 3–20, 2016.
[3]
Y. Liu, L. Wang, J. Cheng, C. Li, and X. Chen, “Multi-focus image fusion: A survey of the state of the art,” Inf. Fusion, vol. 64, pp. 71–91, 2020.
[4]
F. Xu, J. Liu, Y. Song, H. Sun, and X. Wang, “Multi-Exposure Image Fusion Techniques: A Comprehensive Review,” Remote Sens., vol. 14, no. 3, p. 771, 2022.
[5]
X. Deng and P. L. Dragotti, “Deep convolutional neural network for multi-modal image restoration and fusion,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 10, pp. 3333–3348, 2020.
[6]
T. M. Mitchell and T. M. Mitchell, Machine learning, vol. 1, no. 9. McGraw-hill New York, 1997.
[7]
Q. Yao, “Taking human out of learning applications: A survey on automated machine learning,” arXiv Prepr. arXiv1810.13306, 2018.
[8]
G. Luo, “A review of automatic selection methods for machine learning algorithms and hyper-parameter values,” Netw. Model. Anal. Heal. Informatics Bioinforma., vol. 5, pp. 1–16, 2016.
[9]
E. Hazan, A. Klivans, and Y. Yuan, “Hyperparameter optimization: A spectral approach,” arXiv Prepr. arXiv1706.00764, 2017.
[10]
C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown, “Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, pp. 847–855.
[11]
W. Huang and Z. Jing, “Multi-focus image fusion using pulse coupled neural network,” Pattern Recognit. Lett., vol. 28, no. 9, pp. 1123–1132, 2007.
[12]
S. Li, X. Kang, J. Hu, and B. Yang, “Image matting for fusion of multi-focus images in dynamic scenes,” Inf. Fusion, vol. 14, no. 2, pp. 147–162, 2013.
[13]
I. De and B. Chanda, “Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure,” Inf. Fusion, vol. 14, no. 2, pp. 136–146, 2013.
[14]
V. Aslantas and R. Kurban, “Fusion of multi-focus images using differential evolution algorithm,” Expert Syst. Appl., vol. 37, no. 12, pp. 8861–8870, 2010.
[15]
B. Ma, Y. Zhu, X. Yin, X. Ban, H. Huang, and M. Mukeshimana, “Sesf-fuse: An unsupervised deep model for multi-focus image fusion,” Neural Comput. Appl., vol. 33, no. 11, pp. 5793–5804, 2021.
[16]
H. Tang, B. Xiao, W. Li, and G. Wang, “Pixel convolutional neural network for multi-focus image fusion,” Inf. Sci. (Ny)., vol. 433, pp. 125–141, 2018.
[17]
Y. Zhang, Y. Liu, P. Sun, H. Yan, X. Zhao, and L. Zhang, “IFCNN: A general image fusion framework based on convolutional neural network,” Inf. Fusion, vol. 54, pp. 99–118, 2020.
[18]
Y. Yang, Z. Nie, S. Huang, P. Lin, and J. Wu, “Multilevel features convolutional neural network for multifocus image fusion,” IEEE Trans. Comput. Imaging, vol. 5, no. 2, pp. 262–273, 2019.
[19]
B. Gu, W. Li, J. Wong, M. Zhu, and M. Wang, “Gradient field multi-exposure images fusion for high dynamic range image visualization,” J. Vis. Commun. Image Represent., vol. 23, no. 4, pp. 604–610, 2012.
[20]
S. Li, X. Kang, and J. Hu, “Image fusion with guided filtering,” IEEE Trans. Image Process., vol. 22, no. 7, pp. 2864–2875, 2013.
[21]
Z. G. Li, J. H. Zheng, and S. Rahardja, “Detail-enhanced exposure fusion,” IEEE Trans. Image Process., vol. 21, no. 11, pp. 4672–4676, 2012.
[22]
Z. Li, Z. Wei, C. Wen, and J. Zheng, “Detail-enhanced multi-scale exposure fusion,” IEEE Trans. Image Process., vol. 26, no. 3, pp. 1243–1252, 2017.
[23]
J. Ma, Y. Ma, and C. Li, “Infrared and visible image fusion methods and applications: A survey,” Inf. Fusion, vol. 45, pp. 153–178, 2019.
[24]
H. Li, X.-J. Wu, and J. Kittler, “RFN-Nest: An end-to-end residual fusion network for infrared and visible images,” Inf. Fusion, vol. 73, pp. 72–86, 2021.
[25]
J. Li, “DRPL: Deep regression pair learning for multi-focus image fusion,” IEEE Trans. Image Process., vol. 29, pp. 4816–4831, 2020.
[26]
D. Han, L. Li, X. Guo, and J. Ma, “Multi-exposure image fusion via deep perceptual enhancement,” Inf. Fusion, vol. 79, pp. 248–262, 2022.
[27]
H. Xu, J. Ma, and X.-P. Zhang, “MEF-GAN: Multi-exposure image fusion via generative adversarial networks,” IEEE Trans. Image Process., vol. 29, pp. 7203–7216, 2020.
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Published In
February 2024
757 pages
ISBN:9798400709234
DOI:10.1145/3651671
Copyright © 2024 ACM.
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Publication History
Published: 07 June 2024
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- the Shenzhen Science and Technology Innovation Committee
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ICMLC 2024
ICMLC 2024: 2024 16th International Conference on Machine Learning and Computing
February 2 - 5, 2024
Shenzhen, China
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