针对传统超分辨率重建方法稀疏表示依赖大训练样本字典的局限性问题,基于 L 2范数的弱稀疏性特点,提出一种改进的单幅图像自学习超分辨率重建方法。通过自学习建立非金字塔阶梯式训练图像集,采用自定义的方法分别提取训练集中低分辨率...针对传统超分辨率重建方法稀疏表示依赖大训练样本字典的局限性问题,基于 L 2范数的弱稀疏性特点,提出一种改进的单幅图像自学习超分辨率重建方法。通过自学习建立非金字塔阶梯式训练图像集,采用自定义的方法分别提取训练集中低分辨率和相应高分辨率图像特征块及特征像素值;结合 L 2范数的协作表示(collaborative representation,CR)理论和支持向量回归(support vector regression,SVR)技术学习多层超分辨率映射模型。实验结果表明,提出的超分辨率方法不仅可行有效,而且与传统的单幅图像的超分辨率方法比较,其PSNR平均提高了0.06~3.92 dB,SSIM平均提高了0.002 4~0.034 8,从客观数值和主观视觉证明了所提方法的优秀性。展开更多
Abstract This paper studies the problem of minimizing a homogeneous polynomial （form） f（x） over the unit sphere Sn-1 = {x ∈ R^n： ｜｜X｜｜2 = 1}. The problem is NP-hard when f（x） has degree 3 or higher. Denote...Abstract This paper studies the problem of minimizing a homogeneous polynomial （form） f（x） over the unit sphere Sn-1 = {x ∈ R^n： ｜｜X｜｜2 = 1}. The problem is NP-hard when f（x） has degree 3 or higher. Denote by fmin （resp. fmax） the minimum （resp. maximum） value of f（x） on S^n-1. First, when f（x） is an even form of degree 2d, we study the standard sum of squares （SOS） relaxation for finding a lower bound of the minimum .fmin ：max γ s.t.f（x）-γ.｜｜x｜｜2^2d is SOS.Let fos be be the above optimal value. Then we show that for all n ≥ 2d,Here, the constant C（d） is independent of n. Second, when f（x） is a multi-form and ^-1 becomes a muilti-unit sphere, we generalize the above SOS relaxation and prove a similar bound. Third, when f（x） is sparse, we prove an improved bound depending on its sparsity pattern; when f（x） is odd, we formulate the problem equivalently as minimizing a certain even form, and prove a similar bound. Last, for minimizing f（x） over a hypersurface H（g） = {x E lRn： g（x） = 1} defined by a positive definite form g（x）, we generalize the above SOS relaxation and prove a similar bound.展开更多
The orthogonal signals of multi-carrier-frequency emission and multiple antennas receipt module are used in SIAR radar.The corresponding received echo is equivalent to non-uniform spatial sampling after the frequency ...The orthogonal signals of multi-carrier-frequency emission and multiple antennas receipt module are used in SIAR radar.The corresponding received echo is equivalent to non-uniform spatial sampling after the frequency diversity process.As using the traditional Fourier transform will result in the target spectral with large sidelobe,the method presented in this paper firstly makes the preordering treatment for the position of the received antenna.Then,the Bayesian maximum posteriori estimation with l2-norm weighted constraint is utilized to achieve the equivalent uniform array echo.The simulations present the spectrum estimation in angle precision estimation of multiple targets under different SNRs,different virtual antenna numbers and different elevations.The estimation results confirm the advantage of SIAR radar both in array expansion and angle estimation.展开更多
文摘Abstract This paper studies the problem of minimizing a homogeneous polynomial （form） f（x） over the unit sphere Sn-1 = {x ∈ R^n： ｜｜X｜｜2 = 1}. The problem is NP-hard when f（x） has degree 3 or higher. Denote by fmin （resp. fmax） the minimum （resp. maximum） value of f（x） on S^n-1. First, when f（x） is an even form of degree 2d, we study the standard sum of squares （SOS） relaxation for finding a lower bound of the minimum .fmin ：max γ s.t.f（x）-γ.｜｜x｜｜2^2d is SOS.Let fos be be the above optimal value. Then we show that for all n ≥ 2d,Here, the constant C（d） is independent of n. Second, when f（x） is a multi-form and ^-1 becomes a muilti-unit sphere, we generalize the above SOS relaxation and prove a similar bound. Third, when f（x） is sparse, we prove an improved bound depending on its sparsity pattern; when f（x） is odd, we formulate the problem equivalently as minimizing a certain even form, and prove a similar bound. Last, for minimizing f（x） over a hypersurface H（g） = {x E lRn： g（x） = 1} defined by a positive definite form g（x）, we generalize the above SOS relaxation and prove a similar bound.
基金supported by the Specialized Research Fund for the Doc-toral Program of Higher Education (Grant No. 200807010004)the National Natural Science Foundation of China (Grant Nos. 60776795, 60902079, 60902031), the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) (Grant No. IRT0645)
文摘The orthogonal signals of multi-carrier-frequency emission and multiple antennas receipt module are used in SIAR radar.The corresponding received echo is equivalent to non-uniform spatial sampling after the frequency diversity process.As using the traditional Fourier transform will result in the target spectral with large sidelobe,the method presented in this paper firstly makes the preordering treatment for the position of the received antenna.Then,the Bayesian maximum posteriori estimation with l2-norm weighted constraint is utilized to achieve the equivalent uniform array echo.The simulations present the spectrum estimation in angle precision estimation of multiple targets under different SNRs,different virtual antenna numbers and different elevations.The estimation results confirm the advantage of SIAR radar both in array expansion and angle estimation.
