In this paper, we apply alternating minimization method to sparse image reconstruction in compressed sensing. This approach can exactly reconstruct the MR image from under-sampled k-space data, i.e., the partial Fouri...In this paper, we apply alternating minimization method to sparse image reconstruction in compressed sensing. This approach can exactly reconstruct the MR image from under-sampled k-space data, i.e., the partial Fourier data. The convergence analysis of the fast method is also given. Some MR images are employed to test in the numerical experi-ments, and the results demonstrate that our method is very efficient in MRI reconstruction.展开更多
We improve spatially selective noise filtration technique proposed by Xu et al. and wavelet transform scale filtering approach developed by Zheng et al. A novel dyadic wavelet transform filtering method for image deno...We improve spatially selective noise filtration technique proposed by Xu et al. and wavelet transform scale filtering approach developed by Zheng et al. A novel dyadic wavelet transform filtering method for image denoising is proposed. This denoising approach can reduce noise to a high degree while preserving most of the edge features of images. Different types of images are employed to test in the numerical experiments. The experimental results show that our filtering method can reduce more noise contents while maintaining more edges than hard-threshold, soft-threshold filters, Xu’s method and Zheng’s method.展开更多
文摘In this paper, we apply alternating minimization method to sparse image reconstruction in compressed sensing. This approach can exactly reconstruct the MR image from under-sampled k-space data, i.e., the partial Fourier data. The convergence analysis of the fast method is also given. Some MR images are employed to test in the numerical experi-ments, and the results demonstrate that our method is very efficient in MRI reconstruction.
文摘We improve spatially selective noise filtration technique proposed by Xu et al. and wavelet transform scale filtering approach developed by Zheng et al. A novel dyadic wavelet transform filtering method for image denoising is proposed. This denoising approach can reduce noise to a high degree while preserving most of the edge features of images. Different types of images are employed to test in the numerical experiments. The experimental results show that our filtering method can reduce more noise contents while maintaining more edges than hard-threshold, soft-threshold filters, Xu’s method and Zheng’s method.
