关键词: 图像融合, 红外图像, 可见光图像, NSCT, PCA

Abstract: Aiming at the problem that the target details are easily lost in the process of infrared and visible light image fusion, an image fusion algorithm of combining non-subsampled contourlet transform (NSCT) with principal component analysis (PCA) is proposed. First, NSCT is used to decompose the source image to obtain low-frequency and high-frequency sub-band images. In the low-frequency sub-band coefficients, because PCA can highlight the main information of the image, the principal component analysis method fusion rule is selected. In the high-frequency sub-band, relatively high-level coefficients express the most detailed information in the source image, the absolute maximum method fusion rule is selected, while the low-level coefficients represent rougher information, the absolute maximum and regional standard deviation fusion rules can be selected. It can be concluded from the experimental results that the algorithm is superior to other algorithms in the fusion effect of target information and detailed information in infrared and visible images, and has better image visual effects.

Key words: image fusion, infrared image, visible light image, NSCT, PCA