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Application of deep learning model based on image definition in real-time digital image fusion

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Abstract

This paper focuses on pulse coupled neural network (PCNN) and digital image fusion. Aiming at the existing problems, this paper proposes a real-time deep learning model with dual-channel PCNN fusion algorithm based on image definition. It will also be helpful to digital image forensics. With the integration of the orthogonal color space that conforms to HVS, this algorithm simplifies the traditional PCNN model to a parallel dual-channel adaptive PCNN structure. Also, it can realize the adaptive processing by defining the image definition to be β, the coupled linking coefficient. As the dynamic threshold can be increased exponentially with this method, it can effectively solve the problems. The experimental result proves that our algorithm outperforms the traditional fusion algorithms according to the subjective visual effect or the objective assessment standard.

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Authors and Affiliations

  1. College of Computer and Software, Nanjing Institute of Industry Technology, Nanjing, China

    Hui Zhou, Jianhua Peng & Changwu Liao

  2. College of Computer and Information Engineering, Hohai University, Nanjing, China

    Hui Zhou

  3. Tsubaki of Canada, 1630 Drew Rd, Mississauga, ON, Canada

    Jue Li

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  1. Hui Zhou
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  2. Jianhua Peng
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  3. Changwu Liao
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  4. Jue Li
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Correspondence to Hui Zhou.

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Zhou, H., Peng, J., Liao, C. et al. Application of deep learning model based on image definition in real-time digital image fusion. J Real-Time Image Proc 17, 643–654 (2020). https://doi.org/10.1007/s11554-020-00956-1

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  • DOI: https://doi.org/10.1007/s11554-020-00956-1

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