Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing.

Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing.
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DOI:
10.3762/bjnano.3.84
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发表时间:
2012
影响因子:
3.1
通讯作者:
Fantner GE
Fantner GE
中科院分区:
材料科学3区
文献类型:
--
作者:
Erickson BW;Coquoz S;Adams JD;Burns DJ;Fantner GE

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现代高速原子力显微镜可以在短时间内生成大量数据。序列中的每个图像都必须快速准确地处理,以获得样品及其随时间变化的真实表示。本文提出了一种自动化的,自适应算法所需的处理AFM图像。该算法自适应地校正常见的一维失真以及最常见的二维失真。该方法采用迭代阈值处理算法,快速准确地分离背景和表面形貌。这种分离防止了来自地形特征的人为偏差,并确保了序列中不同图像之间的最佳一致性。该方法同样适用于所有通道的AFM数据,并且可以在几秒钟内处理图像。
Modern high-speed atomic force microscopes generate significant quantities of data in a short amount of time. Each image in the sequence has to be processed quickly and accurately in order to obtain a true representation of the sample and its changes over time. This paper presents an automated, adaptive algorithm for the required processing of AFM images. The algorithm adaptively corrects for both common one-dimensional distortions as well as the most common two-dimensional distortions. This method uses an iterative thresholded processing algorithm for rapid and accurate separation of background and surface topography. This separation prevents artificial bias from topographic features and ensures the best possible coherence between the different images in a sequence. This method is equally applicable to all channels of AFM data, and can process images in seconds.
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