Parametric Semi-Blind Deconvolution Algorithm With Huber-Markov Regularization For Passive Millimeter-Wave Images

Passive millimeter-wave (PMMW) images often suffer common problems of noise and blurring. A new method is proposed to estimate the instrument response function (IRF) and desired image simultaneously. The proposed variational model integrates the adaptive weight data term, image smooth term, and IRF smooth term. The major novelty of this work is that Huber-Markov regularization is adopted for PMMW image restoration, which can preserve structural details as well as suppress noise effectively. The IRF is parametrically formulated as a Gaussian-shaped function based on experimental measurements through the utilized PMMW imaging system. The alternation minimization iterative method is applied to achieve the IRF width and desired image. Comparative experimental results with some real PMMW images reveal that the proposed approach can effectively suppress noise, reduce ringing artifacts, and improve the spatial resolution.