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	A Novel Hybrid Total Variation Minimization Algorithm for Compressed Sensing.
	Li, Hongyu; Wang, Yong; Liang, Dong; Ying, Leslie
	2017
会议日期	2017
会议地点	Anaheim, CA
英文摘要	Compressed sensing (CS) is a technology to acquire and reconstruct sparse signals below the Nyquist rate. For images, total variation of the signal is usually minimized to promote sparseness of the image in gradient. However, similar to all L1-minimization algorithms, total variation has the issue of penalizing large gradient, thus causing large errors on image edges. Many non-convex penalties have been proposed to address the issue of L1 minimization For example, homotopic LO minimization algorithms have shown success in reconstructing images from magnetic resonance imaging (MRI). Homotopic LO minimizationmay suffer from local minimum which may not be sufficiently robust when the signal is not strictly sparse or the measurements are contaminated by noise. In this paper, we propose a hybrid total variation minimization algorithm to integrate the benefits of both L1 and homotopic LO minimization algorithms for image recovery from reduced measurements. The algorithm minimizes the conventional total variation when the gradient is small, and minimizes the LO of gradient when the gradient is large. The transition between L I and LO of the gradients is determined by an auto-adaptive threshold. The proposed algorithm has the benefits of L1 minimization being robust to noise/approximation errors, and also the benefits of LO minimization requiring fewer measurements for recovery. Experimental results using MRI data are presented to demonstrate the proposed hybrid total variation minimization algorithm yields improved image quality over other existing methods in terms of the reconstruction accuracy.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12230]
专题	深圳先进技术研究院_医工所
作者单位	2017
推荐引用方式 GB/T 7714	Li, Hongyu,Wang, Yong,Liang, Dong,et al. A Novel Hybrid Total Variation Minimization Algorithm for Compressed Sensing.[C]. 见:. Anaheim, CA. 2017.

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