MR Image Super-Resolution via Wide Residual Networks With Fixed Skip Connection
MR Image Super-Resolution via Wide Residual Networks With Fixed Skip Connection
复制标题
通过具有固定跳跃连接的宽残差网络实现 MR 图像超分辨率
DOI：
10.1109/jbhi.2018.2843819
复制
发表时间：
2019-05-01
影响因子：
7.7
通讯作者：
Yan, Pingkun
中科院分区：
文献类型：
--
作者：
Shi, Jun;Li, Zheng;Yan, Pingkun
空间分辨率是磁共振成像中的关键成像参数。图像超分辨率（SR）是一种有效且经济高效的替代技术，可提高 MR 图像的空间分辨率。在过去的几年里，基于卷积神经网络（CNN）的 SR 方法已经取得了最先进的性能。然而，具有非常深的网络结构的CNN通常会遇到退化和特征重用减少的问题，这增加了网络训练的难度并降低了SR的细节传输能力。为了解决这些问题，在这项工作中，提出了一种基于固定跳跃连接的渐进宽残差网络（称为FSCWRN）的SR算法来重建MR图像，该算法结合了全局残差学习和基于浅层网络的局部残差学习。采用渐进式宽网络策略来替代更深的网络，可以部分缓解上述问题，而固定的跳跃连接有助于从固定的浅层网络到后续网络提供丰富的高频局部细节。在一个模拟 MR 图像数据库和三个真实 MR 图像数据库上的实验结果表明了所提出的 FSCWRN SR 算法的有效性，与其他算法相比，该算法实现了更高的重建性能。
Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image super-resolution (SR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. Over the past several years, the convolutional neural networks (CNN)-based SR methods have achieved state-of-the-art performance. However, CNNs with very deep network structures usually suffer from the problems of degradation and diminishing feature reuse, which add difficulty to network training and degenerate the transmission capability of details for SR. To address these problems, in this work, a progressive wide residual network with a fixed skip connection (named FSCWRN) based SR algorithm is proposed to reconstruct MR images, which combines the global residual learning and the shallow network based local residual learning. The strategy of progressive wide networks is adopted to replace deeper networks, which can partially relax the above-mentioned problems, while a fixed skip connection helps provide rich local details at high frequencies from a fixed shallow layer network to subsequent networks. The experimental results on one simulated MR image database and three real MR image databases show the effectiveness of the proposed FSCWRN SR algorithm, which achieves improved reconstruction performance compared with other algorithms.