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Abstract: A poor inherent resolution capability of the passive millimeter-wave (PMMW) imaging becomes a problem in many applications. This paper proposes a parametric blind deconvolution approach for improving the resolution of PMMW images with low signal-to-noise ratio. Image deconvolution is a challenging ill-posed inverse problem when only partial knowledge of the point spread function (PSF) is available, therefore, regularization techniques need to be used. To restore high quality PMMW image, framelet based regularization constraint is incorporated into parametric blind PMMW image deconvolution framework, which is modeled as an alternative optimization problem about the image and the PSF. The PSF is modeled as a parametric form to restrict the PSF solution space. Furthermore, the split Bregman iteration is used to solve the resulting minimization problem. Comparative experimental results on simulated and real PMMW images show that the proposed method can effectively suppress noise, reduce artifacts, and improve the spatial resolution. [Copyright &y& Elsevier]

Abstract: In the paper, a multiframe blind image deconvolution method based on total variation and framelet regularizer is proposed. An adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problems. In each iteration, four sub-problems need to be solved, one of which can be very efficiently and easily solved via fast Fourier transform implementation or closed form solution. Both simulated noisy and blurred frames and real degraded frames are used to verify the effectiveness of the proposed method. Comparative experimental results show that the proposed method can efficiently remove the blur and noises and restore high quality sharp image. [Copyright &y& Elsevier]