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摘要翻译：
图像修复是指使用相邻像素填充图像中缺失的地方。它在图像处理的不同任务中也有许多应用。这些应用大多通过显著的不必要的改变甚至消除一些现有的像素来增强图像质量。这些变化需要相当大的计算复杂性，从而导致显着的处理时间。在本文中，我们提出了一种快速修复算法，称为色带，基于选择的补丁周围的每一个缺失的像素。这将加快视频图像的在线帧修复的执行速度和能力。所应用的代价函数是所有相邻像素的统计和空间特征的结合。我们使用所提出的代价函数对一些候选补丁进行评估，并将其最小化以获得最终的补丁。实验结果表明，与已有方法相比，“色带”算法具有更高的速度，而对于杂项数据集的图像，其PSNR和SSIM都是相当的。
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英文标题：
《RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics》
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作者：
Mojtaba Akbari, Majid Mohrekesh, Nader Karimi, Shadrokh Samavi
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最新提交年份：
2018
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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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英文摘要：
Image inpainting refers to filling missing places in images using neighboring pixels. It also has many applications in different tasks of image processing. Most of these applications enhance the image quality by significant unwanted changes or even elimination of some existing pixels. These changes require considerable computational complexities which in turn results in remarkable processing time. In this paper we propose a fast inpainting algorithm called RIBBONS based on selection of patches around each missing pixel. This would accelerate the execution speed and the capability of online frame inpainting in video. The applied cost-function is a combination of statistical and spatial features in all neighboring pixels. We evaluate some candidate patches using the proposed cost function and minimize it to achieve the final patch. Experimental results show the higher speed of 'Ribbons' in comparison with previous methods while being comparable in terms of PSNR and SSIM for the images in MISC dataset.
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PDF链接：
https://arxiv.org/pdf/1712.09236
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