Passive Millimeter Wave Image Denoising Algorithm Research

The passive millimeter-wave imaging system obtains the passive millimeter-wave image by receiving target's millimeter-wave radiation energy,so the system can perform detecting and recognizing on the target.Because of the stealthiness and no flicker effect,the passive millimeter-wave imaging system is widely used in conceal surveillance,anti-terrorist,navigation and other fields.Based on the denoising problem of the passive millimeter-wave imaging system,this article does some research on how to denoise a passive millimeter-wave image.Taking the traditional and popular algorithms into consideration,this article pays attention to wiener filtering,wavelet transform based on soft threshold and sparse representation.In order to reduce the residual noise of wiener filtering,a modified block-wise wiener filtering based on different scales and multistage filtering is proposed.What's more,a modified wavelet soft-threshold algorithm based on global shrinkage-threshold and regulatory factor and a modified wavelet soft-threshold algorithm based on adaptive threshold are also proposed to overcome the exceedingly-obliterate of the general soft threshold.A modified adaptive sparse representation denoising algorithm based on difference threshold is proposed to cut down the running time and reduce the image blurring that caused by adaptive sparse representation algorithm.With a purpose of cutting down the running time further and not having too much bad effect on the quality of the denoised image,the doubly sparse representation algorithm is also proposed.The experimental results performed on millimeter-wave image demonstrate the effectiveness of the modified algorithms.