Super-resolution processing of passive millimeter-wave images based on adaptive projected Landweber algorithm

Passive millimeter wave (PMMW) images inherently have the problem of poor resolution owing tolimited aperture dimension. Thus, efficient post-processing is necessary to achieve resolution improvement. Anadaptive projected Landweber (APL) super-resolution algorithm using a spectral correction procedure, which at-tempts to combine the strong points of all of the projected Landweber (PL) iteration and the adaptive relaxationparameter adjustment and the spectral correction method, is proposed. In the algorithm, the PL iterations are im-plemented as the main image restoration scheme and a spectral correction method is included in which the calculatedspectrum within the passband is replaced by the known low frequency component. Then, the algorithm updatesthe relaxation parameter adaptively at each iteration. A qualitative evaluation of this algorithm is performed withsimulated data as well as actual radiometer image captured by 91.5 GHz mechanically scanned radiometer. Fromexperiments, it is found that the super-resolution algorithm obtains better results and enhances the resolution andhas lower mean square error (MSE). These constraints and adaptive character and spectral correction proceduresspeed up the convergence of the Landweber algorithm and reduce the ringing effects that are caused by regularizingthe image restoration problem.