摘要翻译：
去除大量噪声的能力和保留大部分结构的能力是图像平滑器所希望的特性。不幸的是，他们通常似乎彼此不和；一个人只能以牺牲另一个财产为代价来改善一个财产。将M平滑和最小二乘修整相结合，引入TM平滑器，将角点保持特性和离群点鲁棒性统一起来。为了识别保持边缘和角点的性质，提出了一种基于微分几何的新理论。此外，鲁棒性概念被转移到图像处理。在两个例子中，TM-平滑器优于其他保留角点的平滑器。可以从因特网上下载包含TM-和M-smoother的软件包。
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英文标题：
《Outlier robust corner-preserving methods for reconstructing noisy images》
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作者：
Martin Hillebrand, Christine H. M\"uller
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  The ability to remove a large amount of noise and the ability to preserve most structure are desirable properties of an image smoother. Unfortunately, they usually seem to be at odds with each other; one can only improve one property at the cost of the other. By combining M-smoothing and least-squares-trimming, the TM-smoother is introduced as a means to unify corner-preserving properties and outlier robustness. To identify edge- and corner-preserving properties, a new theory based on differential geometry is developed. Further, robustness concepts are transferred to image processing. In two examples, the TM-smoother outperforms other corner-preserving smoothers. A software package containing both the TM- and the M-smoother can be downloaded from the Internet.
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PDF链接：
https://arxiv.org/pdf/708.0481