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Single image deblurring has achieved significant progress for natural daytime images. Saturation is a common phenomenon in blurry images, due to the low light conditions and long exposure times. However, conventional linear deblurring methods usually deal with natural blurry images well but result in severe ringing artifacts when recovering low-light saturated blurry images. To solve this problem, we formulate the saturation deblurring problem as a nonlinear model, in which all the saturated and unsaturated pixels are modeled adaptively. Specifically, we additionally introduce a nonlinear function to the convolution operator to accommodate the procedure of the saturation in the presence of the blurring. The proposed method has two advantages over previous methods. On the one hand, the proposed method achieves the same high quality of restoring the natural image as seen in conventional deblurring methods, while also reducing the estimation errors in saturated areas and suppressing ringing artifacts. On the other hand, compared with the recent saturated-based deblurring methods, the proposed method captures the formation of unsaturated and saturated degradations straightforwardly rather than with cumbersome and error-prone detection steps. Note that, this nonlinear degradation model can be naturally formulated into a maximum-a posterioriframework, and can be efficiently decoupled into several solvable sub-problems via the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real-world images demonstrate that the proposed deblurring algorithm outperforms the state-of-the-art low-light saturation-based deblurring methods. [ABSTRACT FROM AUTHOR]

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]

Abstract: Multi-detectors imaging system often suffers from the problem of the stripe noise, which greatly degrades the quality of the resulting images. To better remove stripe noise and preserve the edge and texture information, a robust destriping algorithm with spatially adaptive unidirectional total variation (SAUTV) model is introduced. The spatial information of the striping noise is detected by using the stripe indicator called difference eigenvalue, and a weighted parameter determined by the difference eigenvalue information is added to constrain the regularization strength of the UTV regularization. The proposed algorithm can effectively remove the stripe noise and preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Split Bregman method is utilized to efficiently solve the resulting minimization problem. Comparative results on simulated and real striped images taken with two kinds of imaging systems are reported. [Copyright &y& Elsevier]