Journal
MICRON
Volume 140, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.micron.2020.102950
Keywords
Atomic force microscope; Vertical drift; Image correction; Morphology prediction
Categories
Funding
- National Natural Science Foundation of China [61633012, 62003172, 21933006]
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This paper proposes an image correction algorithm based on morphology prediction to effectively eliminate vertical drift of atomic force microscope (AFM) images. It includes designing a Gaussian-Hann filter, developing an adaptive image binarization algorithm, and proposing an advanced morphology prediction algorithm.
The atomic force microscope (AFM) has become a powerful tool in many fields. However, environmental noise and other disturbances are very likely to cause the AFM probe to vibrate, which lead to vertical drift in AFM imaging and limit its further application. Therefore, to correct image distortion caused by vertical drift, a morphology prediction based image correction algorithm is proposed in this paper. Specifically, a Gaussian-Hann filter is first designed for distorted AFM images, based on which, an adaptive image binarization algorithm is developed to achieve accurate object detection and background extraction. Furthermore, an advanced morphology prediction algorithm, consisting of morphological approximation prediction and morphological detail prediction, is proposed to correct image distortion by using the extracted substrate of a sample image. Approximate morphology is generated by an improved weighted fusion autoregressive model, and morphological detail is obtained by energy analysis based on discrete wavelet transform. Experimental and application results are presented to illustrate that the proposed algorithm is able to effectively eliminate vertical drift of AFM images.
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