LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations.
LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations.
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LRTV：MR图像超分辨率，具有低级别和总变异正常。
DOI：
10.1109/tmi.2015.2437894
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发表时间：
2015-12
影响因子：
10.6
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Shi F;Cheng J;Wang L;Yap PT;Shen D
图像超分辨率(SR)旨在从低分辨率图像中恢复高分辨率图像，以改进图像分析和可视化。广泛用于此目的的内插方法通常会导致边缘模糊和块效应的图像。更先进的方法，如全变分(TV)，在图像恢复过程中保持边缘清晰度。然而，这些方法只利用来自本地邻域的信息，而忽略了来自远程体素的有用信息。本文提出了一种综合了局部和全局信息的图像随机共振方法，以实现有效的图像恢复。这是通过除电视之外的低级正则化来实现的，该低级正则化使得能够在整个图像中利用信息。利用乘子交替方向法(ADMM)可以有效地解决优化问题。对成人和儿童的MR图像的实验表明，该方法增强了恢复的高分辨率图像的细节，优于最近邻内插、三次内插、迭代反投影(IBP)、非局部平均(NLM)和基于电视的上采样等方法。
Image super-resolution (SR) aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation (TV) retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers (ADMM). Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.