摘要翻译：
去栅栏是消除图像或视频上捕捉到的栅栏，提供一个清晰的场景视图。它被应用于许多目的，包括辅助摄影师和提高计算机视觉算法的性能，如目标检测和识别。然而，现有的去栅栏方法由于栅栏分割的困难以及受摄像机或物体运动的影响，性能有限。为了克服这些问题，我们提出了一种新的方法，包括卷积神经网络分割和快速/鲁棒恢复算法。采用卷积神经网络的分割算法在栅栏分割的精度上取得了显著的提高。利用光流的恢复算法产生了似是而非的去栅栏图像和视频。本文提出的方法在我们的复杂多样的数据集和公开可用的数据集上进行了实验。实验结果表明，该方法在分割和内容恢复方面都达到了最先进的性能。
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英文标题：
《Accurate and efficient video de-fencing using convolutional neural
  networks and temporal information》
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作者：
Chen Du, Byeongkeun Kang, Zheng Xu, Ji Dai and Truong Nguyen
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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：
  De-fencing is to eliminate the captured fence on an image or a video, providing a clear view of the scene. It has been applied for many purposes including assisting photographers and improving the performance of computer vision algorithms such as object detection and recognition. However, the state-of-the-art de-fencing methods have limited performance caused by the difficulty of fence segmentation and also suffer from the motion of the camera or objects. To overcome these problems, we propose a novel method consisting of segmentation using convolutional neural networks and a fast/robust recovery algorithm. The segmentation algorithm using convolutional neural network achieves significant improvement in the accuracy of fence segmentation. The recovery algorithm using optical flow produces plausible de-fenced images and videos. The proposed method is experimented on both our diverse and complex dataset and publicly available datasets. The experimental results demonstrate that the proposed method achieves the state-of-the-art performance for both segmentation and content recovery.
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PDF链接：
https://arxiv.org/pdf/1806.10781