Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dime...Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.展开更多
This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MR...This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MRI method based on compressed sensing （CS） with multiple regularizations （two regularizations including total variation （TV） norm and L1 norm or three regularizations consisting of total variation, L1 norm and wavelet tree structure） is proposed in this paper, which is implemented by applying split augmented lagrangian shrinkage algorithm （SALSA）. To solve magnetic resonance image reconstruction problems with linear combinations of total variation and L1 norm, we utilized composite spht denoising （CSD） to split the original complex problem into TV norm and L1 norm regularization subproblems which were simple and easy to be solved respectively in this paper. The reconstructed image was obtained from the weighted average of solutions from two subprohlems in an iterative framework. Because each of the splitted subproblems can be regarded as MRI model based on CS with single regularization, and for solving the kind of model, split augmented lagrange algorithm has advantage over existing fast algorithm such as fast iterative shrinkage thresholding（FIST） and two step iterative shrinkage thresholding （TWIST） in convergence speed. Therefore, we proposed to adopt SALSA to solve the subproblems. Moreover, in order to solve magnetic resonance image reconstruction problems with linear combinations of total variation, L1 norm and wavelet tree structure, we can split the original problem into three subproblems in the same manner, which can be processed by existing iteration scheme. A great deal of experimental results show that the proposed methods can effectively reconstruct the original image. Compared with existing algorithms such as TVCMRI, RecPF, CSA, FCSA and WaTMRI, the proposed methods have greatly improved the quality of the reconstructed images and have better visual effect.展开更多
基金Supported by the National Natural Science Foundation of China(No.60472058,60975017)the Fundamental Research Funds for the Central Universities(No.2009B32614,2009B32414)
文摘Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.
基金Natural Science Foundation of Chinagrant number:81371635+3 种基金Research Fund for the Doctoral Program of Higher Education of Chinagrant number:20120131110062Shandong Province Science and Technology Development Plangrant number:2013GGX10104
文摘This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MRI method based on compressed sensing （CS） with multiple regularizations （two regularizations including total variation （TV） norm and L1 norm or three regularizations consisting of total variation, L1 norm and wavelet tree structure） is proposed in this paper, which is implemented by applying split augmented lagrangian shrinkage algorithm （SALSA）. To solve magnetic resonance image reconstruction problems with linear combinations of total variation and L1 norm, we utilized composite spht denoising （CSD） to split the original complex problem into TV norm and L1 norm regularization subproblems which were simple and easy to be solved respectively in this paper. The reconstructed image was obtained from the weighted average of solutions from two subprohlems in an iterative framework. Because each of the splitted subproblems can be regarded as MRI model based on CS with single regularization, and for solving the kind of model, split augmented lagrange algorithm has advantage over existing fast algorithm such as fast iterative shrinkage thresholding（FIST） and two step iterative shrinkage thresholding （TWIST） in convergence speed. Therefore, we proposed to adopt SALSA to solve the subproblems. Moreover, in order to solve magnetic resonance image reconstruction problems with linear combinations of total variation, L1 norm and wavelet tree structure, we can split the original problem into three subproblems in the same manner, which can be processed by existing iteration scheme. A great deal of experimental results show that the proposed methods can effectively reconstruct the original image. Compared with existing algorithms such as TVCMRI, RecPF, CSA, FCSA and WaTMRI, the proposed methods have greatly improved the quality of the reconstructed images and have better visual effect.
