Abstract
To avoid loss of detail information and low contrast of fusion results caused by multi-scale transform image fusion algorithm, an image fusion based on masked online convolutional dictionary learning with a surrogate function approach is proposed by introducing image fusion into online convolutional dictionary learning algorithm. The dictionary learning algorithm is used to obtain the over-complete dictionary filter, and then convolutional basis pursuit denoising algorithm is used to obtain the high-frequency and low-frequency sparse coefficients. The fusion image is finally reconstructed. To prove the superiority of our proposed algorithm, six groups of representative infrared and visible images are applied to our method and three comparative methods. The experimental results show that the fusion image of the proposed algorithm achieves good results in subjective and objective quality evaluation. Compared with the JSR-based method, NMI, QTE, and QNCIE increased by 28.04, 19.41, and 0.14% averagely.
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Acknowledgements
The authors would like to thank the editors and anonymous reviewers for their detailed review, valuable comments, and constructive suggestions. This work was supported by the National Natural Science Foundation of China (Grants 61372187), the Research and Practice on Innovation of Police Station Work Assessment System (Grant 18RKX1034), Scientific Research Project of Sichuan Public Security Department (Grant 201917), and the Sichuan Science and Technology Program (Grants 2019YFS0068 and 2019YFS0069).
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Zhang, C., Yang, X. (2021). Image Fusion Based on Masked Online Convolutional Dictionary Learning with Surrogate Function Approach. In: WU, C.H., PATNAIK, S., POPENTIU VLÃDICESCU, F., NAKAMATSU, K. (eds) Recent Developments in Intelligent Computing, Communication and Devices. ICCD 2019. Advances in Intelligent Systems and Computing, vol 1185. Springer, Singapore. https://doi.org/10.1007/978-981-15-5887-0_10
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DOI: https://doi.org/10.1007/978-981-15-5887-0_10
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