Infrared and Visible Image Fusion Based on Sparse Feature

Since the object information can not be extracted efficiently by the traditional infrared and visible image fusion algorithms, an infrared and visible image fusion method based on the non-subsampled shearlet transform and sparse structure features is proposed. Firstly, the source images are decomposed by the non-subsampled shearlet transform. Then, benefit from the advantage of principal component analysis on extracting edge and contour significant features, the fusion rule in low-frequency sub-bands coefficients are merged by using the principal component analysis-based approach. Afterwards, the sparseness based on structural information guides the fusion of high frequency subband coefficient. Finally, the inverse non-subsampled shearlet transform is employed to obtain the fused image. The experimental results demonstrate that the proposed method preserves the background information on visible image and highlights the structural information on infrared image, and improves fusion results effectively.