摘要翻译：
在这篇综述论文中，我们回顾了卷积神经网络(CNNs)在解决成像中的反问题方面的最新应用。近年来，在大规模的图像数据库中训练深度CNNs已经成为可行的方法，它们在目标分类和分割任务中表现出了出色的性能。在这些成功的激励下，研究人员开始将CNNs应用于反问题的解决，如去噪、反卷积、超分辨率和医学图像重建，并开始报告对现有方法的改进，包括基于稀疏性的技术，如压缩感知。在这里，我们回顾了最近在这些领域的实验工作，重点是关键的设计决策：训练数据来自哪里？CNN的架构是什么？学习问题是如何制定和解决的？我们还汇集了一些关键的理论论文，提供了为什么CNNs适合于反问题的观点，并指出了该领域的一些下一步。
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英文标题：
《A Review of Convolutional Neural Networks for Inverse Problems in
  Imaging》
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作者：
Michael T. McCann, Kyong Hwan Jin, Michael Unser
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最新提交年份：
2017
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分类信息：

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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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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英文摘要：
  In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object classification and segmentation tasks. Motivated by these successes, researchers have begun to apply CNNs to the resolution of inverse problems such as denoising, deconvolution, super-resolution, and medical image reconstruction, and they have started to report improvements over state-of-the-art methods, including sparsity-based techniques such as compressed sensing. Here, we review the recent experimental work in these areas, with a focus on the critical design decisions: Where does the training data come from? What is the architecture of the CNN? and How is the learning problem formulated and solved? We also bring together a few key theoretical papers that offer perspective on why CNNs are appropriate for inverse problems and point to some next steps in the field.
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PDF链接：
https://arxiv.org/pdf/1710.04011