A Real Time Image Fusion based Framework for Concealed Weapon Detection

In this paper, a well-organized hidden weapon detection (CWD) algorithm based on image fusion is presented. First, the images obtained consumingdissimilar sensors are decomposed into low and high occurrence bands with the double-density dualtree compound wavelet transform (DDDTCWT). Then two novel decision methods are introduced referring to the appearances of the frequency bands, which meaningfully improves the image fusion performance. The fusion of low frequency bands coefficients is strong-minded by the local contrast, while the high occurrence band fusion rule is developed by considering both the texture feature of HVS and the local energy basis. Finally, the fused image is attained through the inverse DDDTCWT. Experiments and comparisons establish the robustness and efficiency of the proposed approach and indicate that the fusion rules can be applied to different multi-scale transforms. Also, our work shows that the mixture result using the proposed fusion rules on DDDTCWT is superior to other mixtures as well as previously proposed approaches