Research On Denoising And Enhancement Of Millimeter Wave Image

Passive millimeter wave(PMMW)imaging system uses the millimeter wave radiation energy to analyze the target radiation characteristics,then carry out the characteristics and classification research.Because of its strong penetration,non radiation,all-weather and other advantages,PMMW is suitable for the detection of hidden objects in human body.It plays an important role in the security inspection activities in public places such as airports and customs.It is also used widely in the field of international anti-terrorism.However,in the process of millimeter wave image acquisition,due to the influence of antenna aperture,noise pollution and external factors,the actual image resolution is low.In order to improve the image quality and target recognition accuracy,PMMW image denoising and super-resolution processing are necessary.The specific work contents are as follows:Firstly,this paper summarizes the research status of PMMW imaging technology,denoising technology and super-resolution reconstruction technology.Based on the theory of blackbody radiation,the imaging principle and detection theory of passive millimeter wave are analyzed,and the mathematical model of imaging is explored.Secondly,the wavelet transform algorithm is applied to PMMW image information processing.After deeply analyzing the wavelet transform priciple,a new threshold function based on double adjustment factors is proposed.It can effectively overcome the oscillation effect caused by the traditional hard threshold function,and address the problem of fixed difference in soft threshold function.From the theoretical analysis,the superiority and flexibility of the improved algorithm are explained,and the denoising performance of each algorithm is analyzed through experiments.The simulation results show that the improved wavelet thresholding function can make PMMW image softer and have a better subjective visual sense.Its peak signal to noise ratio(PSNR)is 0.67 d B higher than the hard threshold function and 1.06 d B higher than the soft threshold function.Finally,aiming at the problems of millimeter wave image blur and low resolution,the paper studies the super-resolution based convolutional neural network(srcnn).The improvement of network structure of the algorithm from three aspects:the size of convolution kernels,the number of convolution kernels and learning rate.The data set is scaled and rotated to obtain more image feature information.The results show that the improved algorithm has better reconstruction effect and faster convergence speed.In addition,it also has a cetain improvement effect on optical image reconstruction.