Blind image restoration with sparse priori regularization for passive millimeter-wave images

• A blind image restoration method for the passive millimeter-wave images is proposed.
• The regularization item is constructed as the hyper-Laplace function ||x||0.6.
• A data-selected matrix is proposed to estimate the accurate pint spread function.
• The proposed method improves the resolution of the PMMW image.

Passive millimeter wave imaging often suffers from issues such as low resolution, noise, and blurring. In this study, a blind image restoration method for the passive millimeter-wave images (PMMW) is proposed. 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 deblurring methods on both qualitative and quantitative assessments. The proposed method improves the resolution of the PMMW image and makes it more preferable for object recognition.