Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images

Abstract

2013) Parametric semi-blind deconvolution algorithm with Huber-Markov regularization for passive millimeter-wave images, Journal of Modern Optics, 60:12, 970-982, 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.

Blind spectrum reconstruction algorithm withL0-sparse representation

“…However, due to the ill-posed nature of deconvolution, the RL algorithm will amplify the noise with increasing iteration number. Thus, many regularizations such as Tikhonov regularization (TR) [9][10][11], shape priori [12], high-order statistics (HOS), Huber-Markov [13,14], and spatial regularization [15] have been incorporated. In recent years, total variation (TV) and its variants have been popular choices for the regularization terms to solve various blind deconvolution problems [16,17].…”