Abstract: An image fusion method based on non-subsampled contourlet transform (NSCT) and guided filter is proposed to overcome the shortcomings of traditional fusion methods for infrared and visible images with weak gray correlation. The source images are decomposed into multi-scale and multi-directional sub-bands using NSCT, which can separate the feature information contained in different frequency domains to obtain a low-frequency approximate image and the high-frequency directional sub-band images. The local window weighted average energy and sum-modified-Laplacian energy are regarded as the activity measures of low frequency approximate image, respectively, which are used to construct a salient feature map to solve the two key problems of energy preservation and detail extraction. In the directional sub-band image, the maximun activity measure rule is used to obtain the decision maps, the source images are taken as the guided images, and the decision maps are used as the input images for guided filtering. The weight distribution graph is obtained to weight and average the directional sub-band images to reduce the noise sensitivity. Finally, the fused approximate and directional sub-band images are reconstructed by non-subsampled contourlet inverse transform, and the final fusion image is obtained. Some fusion experiments on several sets of infrared and visible images were did, and the objective performance assessments were implemented to fusion results. The experimental results indicate that the proposed method performs better in subjective and objective assessments than a few existing typical fusion techniques，such as guided filter fusion method based on two-scale decomposition, sparse representation fusion method in NSCT domain, cross bilateral filter fusion method based on pixel saliency, convolution neural network fusion method based on deep learning, two-scale fusion method based on saliency detection, and obtains better fusion performance.

Key words: non-subsampledcontourlettransform, guidedfilter, infraredimage, salientfeaturemap, shift-invariance