摘要翻译：
高空间分辨率的磁共振图像(MRI)提供了详细的解剖学信息，通常是精确定量分析所必需的。然而，高空间分辨率通常是以较长的扫描时间、较少的空间覆盖和较低的信噪比为代价的。单幅图像超分辨率(SISR)是一种从单一低分辨率(LR)输入图像恢复高分辨率(HR)细节的技术，近年来深度学习的突破使其得到了极大的改善。本文提出了一种新的神经网络结构--三维密集连接超分辨率网络(DCSRN)来恢复结构脑MR图像的HR特征。通过对1113个被试的数据集的实验，我们证明了我们的网络在恢复4x分辨率降低的图像方面优于双三次插值和其他深度学习方法。
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英文标题：
《Brain MRI Super Resolution Using 3D Deep Densely Connected Neural
  Networks》
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作者：
Yuhua Chen, Yibin Xie, Zhengwei Zhou, Feng Shi, Anthony G.
  Christodoulou, Debiao Li
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis. However, high spatial resolution typically comes at the expense of longer scan time, less spatial coverage, and lower signal to noise ratio (SNR). Single Image Super-Resolution (SISR), a technique aimed to restore high-resolution (HR) details from one single low-resolution (LR) input image, has been improved dramatically by recent breakthroughs in deep learning. In this paper, we introduce a new neural network architecture, 3D Densely Connected Super-Resolution Networks (DCSRN) to restore HR features of structural brain MR images. Through experiments on a dataset with 1,113 subjects, we demonstrate that our network outperforms bicubic interpolation as well as other deep learning methods in restoring 4x resolution-reduced images.
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PDF链接：
https://arxiv.org/pdf/1801.02728