基于自适应稀疏表示的被动毫米波图像恢复

为了克服傅立叶域和小波域正则化方法不能同时保持目标特征和有效滤除噪声的缺点，提出一种被动毫米波图像恢复的新方法。它利用稀疏表示表达信号灵活的特点，对逆滤波后的毫米波图像采用基于奇异值分解的K聚类（K-SVD）算法进行学习，自适应地得到图像恢复需要的基函数。与傅立叶域和小波域正则化方法相比，论文方法采用了自适应的处理方法，因此能够更好地保持目标特征，更有效地抑制噪声，进而更好地恢复图像。将论文方法用于被动毫米波仿真图像的恢复，得到了很好的结果。因此，它是一种有效的被动毫米波成像方法。

Passive millimeter wave image restoration based on adaptive sparse representation

A novel passive millimeter wave image restoration method is proposed,which aims to overcome the shortcoming that Fourier and wavelet domain regularization methods can not de-noise effectively and maintain target features simultaneously.The new method takes advantage of sparse representation′s merit of representing signals flexibly.It learns from the millimeter wave image after inverse filtering by using K-clustering with singular value decomposition(K-SVD) algorithm to obtain basis functions adaptively for image restoration.Comparing with Fourier and wavelet domain regularization methods,the proposed method employs an adaptive method.So it can maintain target features better and de-noise more effectively,which leads to better image restoration.When the method was used in the restoration of simulated passive millimeter image,good result has been obtained.Therefore,it is an effective passive millimeter imaging method.