Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvo...Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.展开更多
This paper derives the bounded real lemmas corresponding to L∞norm and H∞norm(L-BR and H-BR) of fractional order systems. The lemmas reduce the original computations of norms into linear matrix inequality(LMI) probl...This paper derives the bounded real lemmas corresponding to L∞norm and H∞norm(L-BR and H-BR) of fractional order systems. The lemmas reduce the original computations of norms into linear matrix inequality(LMI) problems, which can be performed in a computationally efficient fashion. This convex relaxation is enlightened from the generalized Kalman-YakubovichPopov(KYP) lemma and brings no conservatism to the L-BR. Meanwhile, an H-BR is developed similarly but with some conservatism.However, it can test the system stability automatically in addition to the norm computation, which is of fundamental importance for system analysis. From this advantage, we further address the synthesis problem of H∞control for fractional order systems in the form of LMI. Three illustrative examples are given to show the effectiveness of our methods.展开更多
It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimizatio...It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimization model making use of the redundancy of natural images, by defining a nonlocal concentration regularization term on the gradient. This nonlocal constraint is carefully combined with a gradientsparsity constraint, allowing details throughout the whole image to be removed automatically in a datadriven manner. As variations in gradient between similar patches can be suppressed effectively, the new model has excellent edge preserving, detail removal,and visual consistency properties. Comparisons with state-of-the-art smoothing methods demonstrate the effectiveness of the new method. Several applications,including edge manipulation, image abstraction,detail magnification, and image resizing, show the applicability of the new method.展开更多
针对零范数平滑算法(SL0算法)中最速下降法存在"锯齿现象",尤其是在最优解附近收敛速度较慢的问题,提出一种改进SL0算法的压缩感知重构算法。该算法结合了最速下降法和拟牛顿法的优点,提高了算法的重构精度、收敛速度和信噪...针对零范数平滑算法(SL0算法)中最速下降法存在"锯齿现象",尤其是在最优解附近收敛速度较慢的问题,提出一种改进SL0算法的压缩感知重构算法。该算法结合了最速下降法和拟牛顿法的优点,提高了算法的重构精度、收敛速度和信噪比。为了验证该算法的可行性及有效性,对一维离散信号进行了仿真实验。通过仿真实验,得到了重构信号与原信号的重构误差、信噪比、迭代次数等参数之间的对比图,图示的仿真结果表明,较之于SL0算法,改进的SL0算法在重构精度和收敛速度方面均有所改善,信噪比提高了近5 d B,从而证明了该算法的可行性及有效性。展开更多
基金Partially Supported by National Natural Science Foundation of China(No.61173102)
文摘Motion deblurring is a basic problem in the field of image processing and analysis. This paper proposes a new method of single image blind deblurring which can be significant to kernel estimation and non-blind deconvolution. Experiments show that the details of the image destroy the structure of the kernel, especially when the blur kernel is large. So we extract the image structure with salient edges by the method based on RTV. In addition, the traditional method for motion blur kernel estimation based on sparse priors is conducive to gain a sparse blur kernel. But these priors do not ensure the continuity of blur kernel and sometimes induce noisy estimated results. Therefore we propose the kernel refinement method based on L0 to overcome the above shortcomings. In terms of non-blind deconvolution we adopt the L1/L2 regularization term. Compared with the traditional method, the method based on L1/L2 norm has better adaptability to image structure, and the constructed energy functional can better describe the sharp image. For this model, an effective algorithm is presented based on alternating minimization algorithm.
基金supported by National Natural Science Foundation of China(Nos.61004017 and 60974103)
文摘This paper derives the bounded real lemmas corresponding to L∞norm and H∞norm(L-BR and H-BR) of fractional order systems. The lemmas reduce the original computations of norms into linear matrix inequality(LMI) problems, which can be performed in a computationally efficient fashion. This convex relaxation is enlightened from the generalized Kalman-YakubovichPopov(KYP) lemma and brings no conservatism to the L-BR. Meanwhile, an H-BR is developed similarly but with some conservatism.However, it can test the system stability automatically in addition to the norm computation, which is of fundamental importance for system analysis. From this advantage, we further address the synthesis problem of H∞control for fractional order systems in the form of LMI. Three illustrative examples are given to show the effectiveness of our methods.
基金supported by the National Natural Science Foundation of China (Nos. 61332015, 61373078, 61272245, 61202148, and 61103150)the NSFC-Guangdong Joint Fund (No. U1201258)
文摘It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimization model making use of the redundancy of natural images, by defining a nonlocal concentration regularization term on the gradient. This nonlocal constraint is carefully combined with a gradientsparsity constraint, allowing details throughout the whole image to be removed automatically in a datadriven manner. As variations in gradient between similar patches can be suppressed effectively, the new model has excellent edge preserving, detail removal,and visual consistency properties. Comparisons with state-of-the-art smoothing methods demonstrate the effectiveness of the new method. Several applications,including edge manipulation, image abstraction,detail magnification, and image resizing, show the applicability of the new method.
文摘针对零范数平滑算法(SL0算法)中最速下降法存在"锯齿现象",尤其是在最优解附近收敛速度较慢的问题,提出一种改进SL0算法的压缩感知重构算法。该算法结合了最速下降法和拟牛顿法的优点,提高了算法的重构精度、收敛速度和信噪比。为了验证该算法的可行性及有效性,对一维离散信号进行了仿真实验。通过仿真实验,得到了重构信号与原信号的重构误差、信噪比、迭代次数等参数之间的对比图,图示的仿真结果表明,较之于SL0算法,改进的SL0算法在重构精度和收敛速度方面均有所改善,信噪比提高了近5 d B,从而证明了该算法的可行性及有效性。
