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A comparison of reconstruction methods for undersampled atomic force microscopy images

机译：欠采样原子力显微镜图像重建方法的比较

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摘要

Non-raster scanning and undersampling of atomic force microscopy (AFM) images is a technique for improving imaging rate and reducing the amount of tip-sample interaction needed to produce an image. Generation of the final image can be done using a variety of image processing techniques based on interpolation or optimization. The choice of reconstruction method has a large impact on the quality of the recovered image and the proper choice depends on the sample under study. In this work we compare interpolation through the use of inpainting algorithms with reconstruction based on optimization through the use of the basis pursuit algorithm commonly used for signal recovery in compressive sensing. Using four different sampling patterns found in non-raster AFM, namely row subsampling, spiral scanning, Lissajous scanning, and random scanning, we subsample data from existing images and compare reconstruction performance against the original image. The results illustrate that inpainting generally produces superior results when the image contains primarily low frequency content while basis pursuit is better when the images have mixed, but sparse, frequency content. Using support vector machines, we then classify images based on their frequency content and sparsity and, from this classification, develop a fast decision strategy to select a reconstruction algorithm to be used on subsampled data. The performance of the classification and decision test are demonstrated on test AFM images.
机译：原子力显微镜（AFM）图像的非光栅扫描和欠采样是一种提高成像速率并减少生成图像所需的尖端样品相互作用量的技术。可以使用基于插值或优化的各种图像处理技术来生成最终图像。重建方法的选择对恢复图像的质量有很大影响，正确的选择取决于所研究的样品。在这项工作中，我们比较了通过使用修复算法进行的插值与通过基于压缩感测中通常用于信号恢复的基本跟踪算法进行的基于优化的重构的比较。使用在非光栅AFM中发现的四种不同的采样模式，即行二次采样，螺旋扫描，李沙育扫描和随机扫描，我们对现有图像的数据进行二次采样，并将重建性能与原始图像进行比较。结果表明，当图像主要包含低频内容时，修复通常会产生更好的效果，而当图像具有混合但稀疏的频率内容时，基础追踪会更好。然后，使用支持向量机，根据图像的频率含量和稀疏度对图像进行分类，并从该分类中开发出一种快速决策策略，以选择要用于子采样数据的重构算法。分类和决策测试的性能在测试AFM图像上得到证明。

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