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首页> 外文期刊>Physics in medicine and biology. >Robust edge-directed interpolation of magnetic resonance images.
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Robust edge-directed interpolation of magnetic resonance images.

机译：磁共振图像的鲁棒边缘定向插值。

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摘要

Image interpolation is intrinsically a severely under-determined inverse problem. Traditional non-adaptive interpolation methods do not account for local image statistics around the edges of image structures. In practice, this results in artifacts such as jagged edges, blurring and/or edge halos. To overcome this shortcoming, edge-directed interpolation has been introduced in different forms. One variant, new edge-directed interpolation (NEDI), has successfully exploited the 'geometric duality' that links the low-resolution image to its corresponding high-resolution image. It has been demonstrated that for scalar images, NEDI is able to produce better results than non-adaptive traditional methods, both visually and quantitatively. In this work, we return to the root of NEDI as a least-squares estimation method of neighborhood patterns and propose a robust scheme to improve it. The improvement is twofold: firstly, a robust least-squares technique is used to improve NEDI's performance to outliers and noise; secondly, the NEDI algorithm is extended with the recently proposed non-local mean estimation scheme. Moreover, the edge-directed concept is applied to the interpolation of multi-valued diffusion-weighted images. The framework is tested on phantom scalar images and real diffusion images, and is shown to achieve better results than the non-adaptive methods as well as NEDI, in terms of visual quality as well as quantitative measures.
机译：图像插值是内在的逆问题。传统的非自适应插值方法不考虑图像结构边缘周围的本地图像统计。在实践中，这导致诸如锯齿状边缘，模糊和/或边缘光环的伪影。为了克服这种缺点，已经以不同的形式引入了边缘定向插值。一个变体新的边缘定向插值（NEDI）已成功利用将低分辨率图像链接到其相应的高分辨率图像的“几何二元性”。已经证明，对于标量图像，NEDI能够在视觉上和定量地产生比非自适应传统方法更好的结果。在这项工作中，我们返回NEDI的根源作为邻域模式的最小二乘估计方法，并提出了一种强大的方案来改进它。改进是双重的：首先，使用鲁棒最小二乘技术来改善NEDI对异常值和噪音的性能;其次，NEDI算法与最近提出的非局部平均估计方案扩展。此外，边缘定向的概念应用于多值扩散加权图像的插值。该框架在幻像标量图像和实际扩散图像上测试，并显示出比视觉质量以及定量措施的非自适应方法以及NEDI来实现更好的结果。

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