A Noisy Infrared and Visible Light Image Fusion Algorithm


Yu Shen, Keyun Xiang, Xiaopeng Chen, Cheng Liu, Journal of Information Processing Systems Vol. 17, No. 5, pp. 1004-1019, Oct. 2021  

https://doi.org/10.3745/JIPS.02.0166
Keywords: bilateral filter, Image fusion, Local Area Standard Variance, Nonsubsample Contourlet Transform (NSCT)
Fulltext:

Abstract

To solve the problems of the low image contrast, fuzzy edge details and edge details missing in noisy image fusion, this study proposes a noisy infrared and visible light image fusion algorithm based on non-subsample contourlet transform (NSCT) and an improved bilateral filter, which uses NSCT to decompose an image into a low-frequency component and high-frequency component. High-frequency noise and edge information are mainly distributed in the high-frequency component, and the improved bilateral filtering method is used to process the high-frequency component of two images, filtering the noise of the images and calculating the image detail of the infrared image’s high-frequency component. It can extract the edge details of the infrared image and visible image as much as possible by superimposing the high-frequency component of infrared image and visible image. At the same time, edge information is enhanced and the visual effect is clearer. For the fusion rule of low-frequency coefficient, the local area standard variance coefficient method is adopted. At last, we decompose the high- and low-frequency coefficient to obtain the fusion image according to the inverse transformation of NSCT. The fusion results show that the edge, contour, texture and other details are maintained and enhanced while the noise is filtered, and the fusion image with a clear edge is obtained. The algorithm could better filter noise and obtain clear fused images in noisy infrared and visible light image fusion.


Statistics
Show / Hide Statistics

Statistics (Past 3 Years)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Shen, Y., Xiang, K., Chen, X., & Liu, C. (2021). A Noisy Infrared and Visible Light Image Fusion Algorithm. Journal of Information Processing Systems, 17(5), 1004-1019. DOI: 10.3745/JIPS.02.0166.

[IEEE Style]
Y. Shen, K. Xiang, X. Chen, C. Liu, "A Noisy Infrared and Visible Light Image Fusion Algorithm," Journal of Information Processing Systems, vol. 17, no. 5, pp. 1004-1019, 2021. DOI: 10.3745/JIPS.02.0166.

[ACM Style]
Yu Shen, Keyun Xiang, Xiaopeng Chen, and Cheng Liu. 2021. A Noisy Infrared and Visible Light Image Fusion Algorithm. Journal of Information Processing Systems, 17, 5, (2021), 1004-1019. DOI: 10.3745/JIPS.02.0166.