Infrared and visible image fusion method based on compound decomposition and intuitionistic fuzzy set

• Format: Article
• Language: zho
• Published: The Northwestern Polytechnical University 2021-08-01
• Series: Xibei Gongye Daxue Xuebao
• Subjects: image fusion; non-subsampled contour transform; potential low-rank representation model; intuitionistic fuzzy set
• Online Access: https://www.jnwpu.org/articles/jnwpu/full_html/2021/04/jnwpu2021394p930/jnwpu2021394p930.html

To overcome the shortcomings of traditional image fusion algorithms based on multiscale transform, an infrared and visible image fusion method based on compound decomposition and intuitionistic fuzzy set is proposed. Firstly, the non-subsampled contour transform is used to decompose the source image into low-frequency coefficients and high-frequency coefficients. Then the potential low-rank representation model is used to decompose low-frequency coefficients into basic sub-bands and salient sub-bands, in which the visual saliency map is taken as weighted coefficient. The weighted summation of low-frequency basic sub-bands is used as the fusion rule. The maximum absolute value of low-frequency salient sub-bands is also used as the fusion rule. The two fusion rules are superimposed to obtain low-frequency fusion coefficients. The intuitionistic fuzzy entropy is used as the fusion rule to measure the texture information and edge information of high-frequency coefficients. Finally, the infrared visible fusion image is obtained with the non-subsampled contour inverse transform. The comparison results on the objective and subjective evaluation of several sets of fusion images show that our image fusion method can effectively keep edge information and rich information on source images, thus producing better visual quality and objective evaluation than other image fusion methods.