SEM Resolution Improvement Using Semi-Blind Restoration with Hybrid L1-L2 Regularization

被引:0
|
作者
Lin, Youzuo [1 ]
Kandel, Yudhishthir [2 ]
Zotta, Matthew [3 ,4 ]
Lifshin, Eric [3 ,4 ]
机构
[1] Los Alamos Natl Lab, Los Alamos, NM 87545 USA
[2] Nanojehm Inc, Albany, NY 12203 USA
[3] SUNY Polytech Inst, Coll Nanoscale Sci, Albany, NY 12203 USA
[4] SUNY Polytech Inst, Coll Engn, Albany, NY 12203 USA
关键词
Scanning Electron Microscope; Image Restoration; Blind Deconvolution; Total-Variation Regularization; Tikhonov Regularization; IMAGE-RESTORATION; MINIMIZATION; ALGORITHM;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Scanning electron microscopy (SEM) resolution is limited by many factors that include sample specific properties, microscope stability, noise, the three dimensional nature of the sample and the excitation volume, and the spatial distribution of electrons in the probe known as the point spread function (PSF). If all, but the latter are optimized, the loss of resolution is principally due to blurring by the convolution of the PSF with the structure of interest. Image resolution can then be increased by deconvolution combined with the mathematical process known as regularization. To accomplish this task, a novel high resolution semi-blind image restoration technique incorporating hybrid L-1 and L-2 regularization terms has been developed. The original optimization is divided into the efficient solution of three subproblems, and has been validated with a variety of actual SEM images.
引用
收藏
页码:33 / 36
页数:4
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