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NanoSIMS image enhancement by reducing random noise using low‐rank method
Author(s) -
Lin Yi,
Hao Jialong,
Miao Zhongzheng,
Zhang Jinhai,
Yang Wei
Publication year - 2020
Publication title -
surface and interface analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 90
eISSN - 1096-9918
pISSN - 0142-2421
DOI - 10.1002/sia.6736
Subject(s) - image (mathematics) , noise (video) , noise reduction , random noise , filter (signal processing) , minification , median filter , computer science , artificial intelligence , algorithm , biological system , pattern recognition (psychology) , mathematics , materials science , computer vision , image processing , mathematical optimization , biology
NanoSIMS images are usually affected by random noises because of various types of sources, which degrade the quality of ion images and increase the uncertainty of the geochemical interpretations. Here, we applied the weighted nuclear norm minimization (WNNM) method to reduce the random noise in the NanoSIMS image. The low‐rank property of the image is fully considered to suppress random noise while retaining reliable details of weak signals. Numerical experiments on four different kinds of NanoSIMS ion images show that the denoising ability of the WNNM method is superior to that of the median filter, no matter the size of the filtering windows used (eg, 3 × 3, 5 × 5, and 7 × 7). The WNNM method can reduce random noise while preserving the most critical details in the original NanoSIMS observations, which can significantly enhance reliability when distinguishing critical boundaries and structures.
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