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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Adaptive and robust statistical methods for processing near-field scanning microwave microscopy images.

K J Coakley¹, A Imtiaz¹, T M Wallis¹

¹National Institute of Standards and Technology, Boulder, CO 80305, USA.

Ultramicroscopy

|December 3, 2014

Summary

This summary is machine-generated.

We developed new image processing techniques for near-field scanning microwave microscopy. These methods effectively remove noise and artifacts, improving material characterization and analysis for large datasets.

Keywords:

Adaptive weights smoothing Atomic force microscopy Denoising Ferrite materials GaN nanowire Gwyddion Leveling Local regression and likelihood Micro-capacitance calibration image Near-field scanning probe microwave microscopy Robust statistical methods Scan artifacts Statistical image processing

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Area of Science:

Materials Science
Nanotechnology
Physics

Background:

Near-field scanning microwave microscopy (NSMM) is crucial for materials characterization.
NSMM images often suffer from noise and artifacts like scan line variations and sample tilt errors.
Analyzing large datasets manually is impractical.

Purpose of the Study:

To develop robust image processing methods for NSMM data.
To improve the accuracy and efficiency of material and device physics studies using NSMM.
To enable automated analysis of large-scale NSMM image datasets.

Main Methods:

Image leveling using robust local regression to estimate and remove 2D trends.
Denoising using Adaptive Weights Smoothing (AWS) to preserve image features.
Demonstration on topography and microwave |S11| images.

Main Results:

The proposed methods effectively remove noise and artifacts from NSMM images.
Robust local regression accurately estimates and corrects for planar trends.
AWS successfully smooths noise while maintaining critical edge features.
Outperformed existing methods in Gwyddion for a challenging test case.

Conclusions:

The developed image processing techniques enhance the reliability of NSMM data analysis.
These methods are suitable for large datasets, reducing the need for manual intervention.
The approach facilitates more accurate material and device characterization using NSMM.