.png){kind=icon}
返回
Fast AFM Imaging Based on Compressive Sensing Using Undersampled Raster Scan
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2020.3023215.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Atomic force microscope (AFM), which has nanoscale precision, is a widely used instrument in material science and biomedical science. Nevertheless, conventional AFM scanning is a time-consuming procedure. Compressive sensing (CS) and undersampling techniques have been introduced to accomplish fast AFM imaging in recent years. At present, existing undersampled scan patterns cannot simultaneously guarantee imaging efficiency and quality well. Therefore, a novel one called undersampled raster (USR) scan is put forward in this article. It has higher imaging efficiency than most existing scan patterns, which is drawn by calculating the scanning time with the proposed estimation formulas. Experimental results show that the imaging quality is obviously better than using other combinations of fast scan patterns and reconstruction algorithms when the regular form of the USR scan is employed with Total Variation Minimization by Augmented Lagrangian and Alternating Direction Algorithm (TVAL3). The universality of this imaging scheme is verified by using a variety of samples. In the end, the applications of regular USR scan or its variant for super-resolution AFM imaging and predicting appropriate sampling rates are proposed. In conclusion, applying regular USR scan and TVAL3 algorithm to CS-based AFMcan effectively realize fast and high-quality imaging.
Keyword:
Atomic force microscope (AFM)
compressive sensing (CS)
reconstruction algorithm
scanning time
undersampled raster (USR) scan
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
.png){kind=icon}
Normative pediatric visual acuity using single surrounded HOTV optotypes on the Electronic Visual Acuity Tester following the Amblyopia Treatment Study protocol根据弱视治疗研究方案，在电子视力测试仪上使用单个包围的HOTV视标的儿童视力
OAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容