Vol.67, No.1, 2021, pp.613-624, doi:10.32604/cmc.2021.013457
Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network
  • Jingming Xia1, Yi Lu1, Ling Tan2,*, Ping Jiang3
1 School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
3 Western University, London, N6A 3K7, Canada
* Corresponding Author: Ling Tan. Email:
Received 07 August 2020; Accepted 12 September 2020; Issue published 12 January 2021
Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse coupled neural network (IPCNN) algorithm, which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm, improving the performance of sparse representation with details injection. The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.
Image fusion; infrared image; visible light image; non-downsampling shear wave transform; improved PCNN; convolutional sparse representation
Cite This Article
J. Xia, Y. Lu, L. Tan and P. Jiang, "Intelligent fusion of infrared and visible image data based on convolutional sparse representation and improved pulse-coupled neural network," Computers, Materials & Continua, vol. 67, no.1, pp. 613–624, 2021.
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