Passive Millimeter Wave Detection And Fusion Based On Mixing Orthogonal Two-direction Wavelet Packet

The increasingly emerging terrorism attacks and violence crimes throughout the world,the safety of public places is attracting more and more attention.Currently used in public places of traditional X-ray security checkpoint,due to the damage to human body health,only to detect the baggage,whether carried dangerous inconvenience to detect the human body.Traditional metal detector can detect metal objects,but can't detect the chemical reagents?ceramic products.Passive Millimeter wave Detection Imaging Machine,by receiving their target millimeter wave radiation temperature,using synthetic aperture imaging real-time,can detect the human body the stash under clothing,including metal,nonmetal,and the gel explosives,petrol and other chemical reagents,protected the safety of public places.It is one of the important research directions in the current security detection technology research.But the passive millimeter wave detector to detect target radiation temperature of the object,the radiation temperature boundary of objects low resolution.The image of Passive millimeter wave is blurry.In view of this problem,to combine the visible light image and millimeter wave image,combined the advantages of both,and the fusion image can clearly show the hidden items,the staff can according to the fusion results to identify the body carrying the location of the hidden objects,further protected the safety of public places.For this reason,this paper devoted to studies the fusion algorithm for visible light/millimeter wave image.The main research contents and results were as follows:The fusion algorithm combines the Mixing Orthogonal Two-direction Wavelet Packet and Pulse Coupled Neural Network(PCNN).Firstly,the image is decomposed by Mixing Orthogonal Two-direction Wavelet Packet,obtained the coefficient of the image in different frequency bands.Second,according to the different coefficients,different fusion rules are used to deal with the coefficients.The fusion rule for low frequency is based on absolute value of gray.And the fusion rule for high frequency is PCNN model.Finally,the coefficients are reconstructed to obtain the fusion image.The experiment indicated that compared with traditional method,the new fusion algorithm is better at recognized the hidden items.Based on the above research,considering that high scale image decomposition can produce a large number of subgraphs,resulting in difficulty in storage,low efficiency in the fusion processing.Therefore,study the application of CS in image fusion,and used to fuse the high frequency.The fusion rule for low frequency is based on absolute value of gray.Finally,the coefficients are reconstructed to obtain the fusion image.The experiment indicated that new algorithm not only improve the efficiency of fusion,but also obtained the high quality fusion results.