Infrared and visible image fusion scheme based on NSCT and low-level visual features

Multi-scale transform (MST) is an efficient tool for image fusion. Recently, many fusion methods have been developed based on different MSTs, and they have shown potential application in many fields. In this paper, we propose an effective infrared and visible image fusion scheme in nonsubsampled contourlet transform (NSCT) domain, in which the NSCT is firstly employed to decompose each of the source images into a series of high frequency subbands and one low frequency subband. To improve the fusion performance we designed two new activity measures for fusion of the lowpass subbands and the highpass subbands. These measures are developed based on the fact that the human visual system (HVS) percept the image quality mainly according to its some low-level features. Then, the selection principles of different subbands are presented based on the corresponding activity measures. Finally, the merged subbands are constructed according to the selection principles, and the final fused image is produced by applying the inverse NSCT on these merged subbands. Experimental results demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art fusion methods in terms of both visual effect and objective evaluation results.