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Yuqing He, Shuaiying Wei, Tao Yang, Weiqi Jin, Mingqi Liu, Xiangyang Zhai. Feature-Based Fusion of Dual Band Infrared Image Using Multiple Pulse Coupled Neural Network[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(1): 129-136. doi: 10.15918/j.jbit1004-0579.17165
Citation: Yuqing He, Shuaiying Wei, Tao Yang, Weiqi Jin, Mingqi Liu, Xiangyang Zhai. Feature-Based Fusion of Dual Band Infrared Image Using Multiple Pulse Coupled Neural Network[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(1): 129-136.doi:10.15918/j.jbit1004-0579.17165
shu

Feature-Based Fusion of Dual Band Infrared Image Using Multiple Pulse Coupled Neural Network

doi:10.15918/j.jbit1004-0579.17165

  • Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2018-03-29
    Abstract
  • To improve the quality of the infrared image and enhance the information of the object, a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network (multi-PCNN)is proposed. In this multi-PCNN fusion scheme, the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN, whose input could be original infrared image. Meanwhile, to make the PCNN fusion effect consistent with the human vision system, Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN. After that, the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image. Compared to wavelet transforms, Laplacian pyramids and traditional multi-PCNNs, fusion images based on our method have more information, rich details and clear edges.
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    • References (16)
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