Acta Photonica Sinica ›› 2019, Vol. 48 ›› Issue (6): 610001-0610001.doi: 10.3788/gzxb20194806.0610001

• Image Processing • Previous Articles     Next Articles

Infrared and Low-light-level Visible Image Fusion Algorithm Based on Contrast Enhancement and Cauchy Fuzzy Function

JIANG Ze-tao1,2, HE Yu-ting1, ZHANG Shao-qin3   

  1. 1. Key Laboratory of Image and Graphic Intelligent Processing of Higher Education in Guangxi, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China;
    2. Key Laboratory of Dependable Software of Guangxi, Guilin University of Electronic Technology, Guilin, Guangxi 541004 China;
    3. Nanchang Hangkong University, Nanchang 330063, China
  • Received:2018-12-18 Online:2019-06-25 Published:2019-03-20
  • Supported by:

    The National Nature Science Foundation of China (Nos. 61572147,61876049,61762066),Project of Science and Technology Plan in Guangxi (No. AC16380108),Project of Intelligent Processing Key Laboratory of Image Graphics Intelligent Processing of Guangxi(Nos. GIIP201701, GIIP201801, GIIP201802, GIIP201803), Project of Postgraduate Innovate Plan in Guangxi (No. 2018YJCX46)

Abstract:

Due to the poor visibility of visible images in low-light environment, an image fusion algorithm based on contrast enhancement and cauchy fuzzy function is proposed to improve the fusion effect of infrared and low-light-level visible images. Firstly, the visibility of dark region of low-light-level visible image is improved by the adaptive enhancement of improved guided filtering. Secondly, non-subsampled shearlet transform is used to decompose infrared and enhanced low-light-level visible images to obtain corresponding low-frequency and high-frequency components. Then, the intuitive fuzzy sets were used to construct the cauchy membership function and adaptive dual-channel spiking cortical model to fuse the low-frequency and high-frequency components. Finally, the fusion image are reconstructed by using non-subsampled shearlet inverse transform. Experimental results show that compared with other fusion algorithms, the algorithm can effectively enhance the dark area of the low-light-level visible image and retain more background information, thus improving the contrast and clarity of the fusion image.

Key words: Image processing, Image fusion, Guided filtering, Adaptive dual-channel spiking cortical model, Cauchy fuzzy function, Non-subsampled shearlet transform

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