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Volume 44 Issue 1
Jan.  2022
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DING Jian, GAO Qingwei, LU Yixiang, SUN Dong. Infrared and Visible Image Fusion Algorithm Based on the Decomposition of Robust Principal Component Analysis and Latent Low Rank Representation[J]. Infrared Technology , 2022, 44(1): 1-8.
Citation: DING Jian, GAO Qingwei, LU Yixiang, SUN Dong. Infrared and Visible Image Fusion Algorithm Based on the Decomposition of Robust Principal Component Analysis and Latent Low Rank Representation[J]. Infrared Technology , 2022, 44(1): 1-8.
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Infrared and Visible Image Fusion Algorithm Based on the Decomposition of Robust Principal Component Analysis and Latent Low Rank Representation

  • School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China

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  • Received Date: October 12, 2020
  • Revised Date: March 29, 2021
    Graphical Abstract
    • Abstract
  • The fusion of infrared and visible images plays an important role in video surveillance, target tracking, etc. To obtain better fusion results for images, this study proposes a novel method combining deep learning and image decomposition based on a robust low-rank representation. First, robust principal component analysis is used to denoise the training set images. Next, rapid latent low rank representation is used to learn a sparse matrix to extract salient features and decompose the source images into low-frequency and high-frequency images. The low-frequency components are then fused using an adaptive weighting strategy, and the high-frequency components are fused by a VGG-19 network. Finally, the new low-frequency image is superimposed with the new high-frequency image to obtain a fused image. Experimental results demonstrate that this method has advantages in terms of both the subjective and objective evaluation of image fusion.
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    • References (27)
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