Superresolution Of Millimeter-Wave Images By Iterative Blind Maximum Likelihood Restoration

The need for superresolution processing of images in muItispectral seeker environments for facilitating smart munition guidance is being increasingly recognized, particularly when the sensor suite includes Millimeter-Wave (MMW) sensors with rather poor inherent resolution capabilities. Despite the technological breakthroughs being made in advanced radiometer designs, the inherent problems associated with diffraction limited imaging impose limitations on the resolution of acquired imagery thus necessitating efficient post-processing to achieve resolution improvements needed for reliable target detection, classification and aimpoint selection. Quantitative results from a recent project directed to superresolution processing of passive MMW images obtained from a 95 GHZ 1-foot diameter aperture radiometer are presented in this paper. The spectral extrapolation performance resulting from the implementation of an iterative Maximum Likelihood (ML) restoration algorithm is demonstrated and the robustness of the algorithm that facilitates a blind implementation useful in scenarios characterized by an incomplete knowledge of sensor point spread function is highlighted.