正规化(语言学)
压缩传感
迭代重建
计算
算法
计算机科学
全变差去噪
数学
规范(哲学)
人工智能
图像(数学)
数学优化
政治学
法学
作者
Junzhou Huang,Shaoting Zhang,Dimitris N. Metaxas
标识
DOI:10.1016/j.media.2011.06.001
摘要
In this paper, we propose an efficient algorithm for MR image reconstruction. The algorithm minimizes a linear combination of three terms corresponding to a least square data fitting, total variation (TV) and L1 norm regularization. This has been shown to be very powerful for the MR image reconstruction. First, we decompose the original problem into L1 and TV norm regularization subproblems respectively. Then, these two subproblems are efficiently solved by existing techniques. Finally, the reconstructed image is obtained from the weighted average of solutions from two subproblems in an iterative framework. We compare the proposed algorithm with previous methods in term of the reconstruction accuracy and computation complexity. Numerous experiments demonstrate the superior performance of the proposed algorithm for compressed MR image reconstruction.
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