Passive millimeter-wave images (PMMW) often suffer from issues such as low resolution, noise, and blurring. In this paper, we proposed a blind image deconvolution method for the passive millimeter-wave images. The purpose of the proposed method is to simultaneously solve the point spread function (PSF) and restoration image. In this method, the data fidelity item is constructed based on Gaussian noise assuming, and the regularization item is constructed as the hyper-Laplace function ‖x‖ 0.6 , which is fitted according to the high-resolution PMMW images. Moreover, a data-selected matrix is proposed to select the regions that are helpful for estimating the accurate PSF. The proposed method has been applied to simulated and real PMMW image experiments. Comparative results demonstrate that the proposed method significantly outperforms the state-of-the-art deconvolution methods on both qualitative and quantitative assessments.
['Tingting Liu', 'Zengzhao Chen', 'H. H. Liu', 'Sanya Liu', 'Zhaoli Zhang', 'Taihe Cao']
Robust blind deconvolution for PMMW images with sparsity presentation