Iteratively reweighted blind deconvolution for passive millimeter-wave images

Passive millimeter-wave (PMMW) imaging frequently suffers from blurring and low resolution due to the diffraction limits and the long wavelengths compared with visual and infrared radiation. Moreover, the observed image is inevitably degraded by the system noise and natural clutter noise. The blur and noise limit the capability of PMMW images in practical applications. In this study, we propose an iteratively reweighted blind deconvolution method for obtaining high quality PMMW images. A weighted least-squares data-fidelity term that is robust to modeling error and a weighted bilateral total variation regularization term are incorporated into the variational blind deconvolution framework. Furthermore, we impose an appropriate smooth constraint for the point spread function of the imaging system. In addition, to improve the practicality of the method, we derive a formula to automatically update the regularization parameter from the data. Comparative experimental results on simulated and real images show that the proposed method is superior to the state-of-the-art methods in terms of both subjective measure and visual quality.