Fusion of infrared and visible images based on NSUDCT

Aiming at the infrared and visible images in a same scene, a novel fusion algorithm based on the nonsubsampled uniform discrete curvelet transform (NSUDCT) was proposed. First, the source images were segmented using the marker controlled watershed segmentation (MCWS), and the joint region graph was obtained by superimposing the segmented results. Then, the nonsubsampled uniform discrete Curvelet transform was applied to the source images, the low-frequency coefficients were fused with the measurement of ratio of region contrast and region standard deviation, the high-frequency directional coefficients were fused with the local energy fusion rule, and the consistency of the fused coefficients was examined. Finally, the fused image was reconstructed from the subband fused coefficients. The experiment results indicate that the proposed method could provide better fusion quality in terms of both visual and quantified measure. Compared with the pixel fusion method based on NSUDCT, the Entropy of fused images increased by 9.87%, the Cross Entropy decreased by 68.04% and the Mutual Information increased by 80%.