Multisensor concealed weapon detection using the image fusion approach

In this paper, an efficient concealed weapon detection (CWD) algorithm based on image fusion is presented. First, the images obtained using different sensors are decomposed into low and high frequency bands with the double-density dual-tree complex wavelet transform (DDDTCWT). Then two novel decision methods are introduced referring to the characteristics of the frequency bands, which significantly improves the image fusion performance for CWD application. The fusion of low frequency bands coefficients is determined by the local contrast, while the high frequency band fusion rule is developed by considering both the texture feature of the human visual system (HVS) and the local energy basis. Finally, the fused image is obtained through the inverse DDDTCWT. Experiments and comparisons demonstrate the robustness and efficiency of the proposed approach and indicate that the fusion rules can be applied to different multiscale transforms. Also, our work shows that the fusion result using the proposed fusion rules on DDDTCWT is superior to other combinations as well as previously proposed approaches.