Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Adaptive and Robust Statistical Methods for Processing Near-Field Scanning Microwave Microscopy Images



Kevin J. Coakley, Samuel Berweger, Atif A. Imtiaz, Thomas Mitchell (Mitch) Wallis, Joel Weber, Pavel Kabos


Near-field scanning microwave microscopy offers great potential to facilitate characterization, development and modeling of materials. By acquiring microwave images at multiple frequencies and amplitudes (along with the other modalities) one can study material and device\physics at different lateral and depth scales. Images are typically noisy and contaminated by artifacts that can vary from scan line to scan line and planar-like trends due to sample tilt errors. Here, we level images based on an estimate of a smooth 2-d trend determined with a robust implementation of a local regression method. In this robust approach, features and outliers which are not due to the trend are automatically downweighted. We denoise images with the Adaptive Weights Smoothing method. This method smooths out additive noise while preserving edge-like features in images. We demonstrate the feasibility of our methods on topography images and microwave S11 images. For one challenging test case, we demonstrate that our method outperforms alternative methods from the scanning probe microscopy data analysis software package Gwyddion. Our methods should be useful for massive image data sets where manual selection of landmarks or image subsets by a user is impractical.


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


Coakley, K. , Berweger, S. , Imtiaz, A. , Wallis, T. , Weber, J. and Kabos, P. (2014), Adaptive and Robust Statistical Methods for Processing Near-Field Scanning Microwave Microscopy Images, Ultramicroscopy, [online], (Accessed February 27, 2024)
Created November 22, 2014, Updated February 9, 2023