摘要翻译：
基于插值的图像缩放方法具有较高的执行速度和较低的计算复杂度。然而，在许多情况下，缩放后的图像质量并不令人满意。超分辨率方法的主要挑战是为图像创建新的细节。本文提出了一种利用放大-缩小-放大策略来创建新细节的新算法。该策略允许通过将估计误差添加到最终图像中来减少模糊效果。对自然图像的实验结果证实了该算法能够产生视觉愉悦的结果。
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英文标题：
《EDIZ: An Error Diffusion Image Zooming Scheme》
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作者：
Soroush Saryazdi, Saman Saryazdi, Hossein Nezamabadi-pour
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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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英文摘要：
  Interpolation based image zooming methods provide a high execution speed and low computational complexity. However, the quality of the zoomed images is unsatisfactory in many cases. The main challenge of super- resolution methods is to create new details to the image. This paper proposes a new algorithm to create new details using a zoom-out-zoom-in strategy. This strategy permits reducing blurring effects by adding the estimated error to the final image. Experimental results for natural images confirm the algorithm's ability to create visually pleasing results.
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PDF链接：
https://arxiv.org/pdf/1712.00855