Sparse MRI Reconstruction via Different Norms Based on Total Variation
In a wide variety of imaging applications (especially medical imaging), the theory of compressed sensing has shown it is surprisingly possible to reconstruct the entire original image from a partial set or subset of the Fourier transform of an image, if the image has a sparse or nearly sparse representation in some transform domain. Recently many fast and efficient algorithms have been proposed by solving a convex e1-minimization problem, which is often used as a scheme of the image recovery. In our work, we will introduce other methods in which the e1 norm isreplaced by the ep norm for p e (0, 1) and the log-sum penalty function, and then perform numerical experiments to compare their performance.
Pengpeng Hao Qiusheng Lian Yanyan Gao
Institute of Information science and technology, Yanshan University, Qinhuangdao, 066004
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
1579-1583
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）