摘要翻译：
高分辨率(HR)磁共振图像(MRI)为临床应用和定量图像分析提供了重要的解剖学信息。然而，HR MRI通常是以较长的扫描时间、较小的空间覆盖和较低的信噪比为代价的。近年来的研究表明，单图像超分辨率(SISR)是一种从单幅低分辨率(LR)输入图像中恢复HR细节的技术，它可以借助先进的深度卷积神经网络(CNN)提供高质量的图像细节。然而，深度神经网络消耗内存大，运行速度慢，尤其是在3D设置下。本文提出了一种新的三维神经网络设计，即具有生成对抗网络(GAN)指导训练的多级密集连接超分辨率网络(mDCSRN)。MDSRN快速训练和推理，GAN促进真实感输出，与原始HR图像几乎无法区分。我们在一个有1113名受试者的数据集上的实验结果表明，我们的新架构在恢复4倍分辨率降级的即时信息方面优于其他流行的深度学习方法，运行速度快6倍。
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英文标题：
《Efficient and Accurate MRI Super-Resolution using a Generative
  Adversarial Network and 3D Multi-Level Densely Connected Network》
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作者：
Yuhua Chen, Feng Shi, Anthony G. Christodoulou, Zhengwei Zhou, Yibin
  Xie, 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：
  High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical information important for clinical application and quantitative image analysis. However, HR MRI conventionally comes at the cost of longer scan time, smaller spatial coverage, and lower signal-to-noise ratio (SNR). Recent studies have shown that single image super-resolution (SISR), a technique to recover HR details from one single low-resolution (LR) input image, could provide high-quality image details with the help of advanced deep convolutional neural networks (CNN). However, deep neural networks consume memory heavily and run slowly, especially in 3D settings. In this paper, we propose a novel 3D neural network design, namely a multi-level densely connected super-resolution network (mDCSRN) with generative adversarial network (GAN)-guided training. The mDCSRN quickly trains and inferences and the GAN promotes realistic output hardly distinguishable from original HR images. Our results from experiments on a dataset with 1,113 subjects show that our new architecture beats other popular deep learning methods in recovering 4x resolution-downgraded im-ages and runs 6x faster.
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PDF链接：
https://arxiv.org/pdf/1803.01417