Fractional-order total variation for improving image fusion based on saliency map

  • Qiaolu Wang
  • Zhisheng GaoEmail author
  • Chunzhi Xie
  • Gongping Chen
  • Qingqing Luo
Original Paper


The fusion of infrared and visible images is difficult because of their different modalities. Current fusion methods are difficult to maintain both complementary information and good visual effects, such as methods of region discrimination based on visual saliency and methods based on total variation (TV). Among them, methods of region discrimination based on visual saliency for fusion have better complementary information but poor visual consistency, while methods based on total variation for fusion have good visual consistency, but there is no proper regularization to ensure sufficient selection and fusion of complementary information. In this paper, an improved infrared and visible image fusion method via visual saliency and fractional-order total variation is proposed. First, the infrared and visible images are fused through the saliency map to obtain a fused image, and then the fused image and an selected original image are fused by the fractional-order total variation to obtain the final fused image. In this paper, visual saliency map-based fusion makes the fused image contain as much complementary information as possible from the source image, while fractional-order total variation-based fusion makes the fused image have better visual effects. Compared with the state-of-the-art image fusion algorithm, the experimental results show that the proposed method is more competitive in retaining image texture details and having visual effects.


Image fusion Visual saliency map-based fusion Fractional gradient Fractional-order total variation-based image fusion 



This work has been partially supported by the Ministry of education Chunhui Project (Grant No. Z2016149), the Key scientific research fund of Xihua University (Grant No. Z17134), Xihua University Graduate Innovation Fund Research Project (Grant Nos. ycjj2018067, ycjj2018044), and Sichuan science and technology program (Grant No. 2019YFG0108).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Computer and Software EngineeringXihua UniversityChengduPeople’s Republic of China

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