Maximum Likelihood Frequency Domain Correction Super-resolution Algorithm for Passive Millimeter Wave Imaging
无源毫米波成像最大似然频域校正超分辨算法

The problem of poor resolution of acquired image in the passive millimeter wave imaging stems mainly from antenna size limitations, thus necessitating some efficient post-processing to achieve resolution improvements. A maximum likelihood (ML) super-resolution algorithm based on frequency domain correction is proposed. First, we employ Wiener filter to restore passband spectrum, then we implement Richardson-Lucy algorithm to complete spectral extrapolation, lastly we implement a spatial spectrum correction algorithm in which the calculated spectrum within the passband is replaced by the low frequency component restored by Wiener filter. Experimental results demonstrate the algorithm improves the convergent rate and enhances the resolution and reduces the ringing effects which are caused by regularizing the image restoration problem. Furthermore, the algorithm is easily implemented for passive millimeter wave imaging.