ACMD Seminar: Toward Parameter-free Restoration of Noisy Orientation Maps

Prashant Athavale
Assistant Professor, Mathematics Dept., Clarkson University

Tuesday, March 19, 2024, 3:00-4:00 PM ET (1:00-2:00 PM MT)

A video of this talk will be made available to NIST staff in the Math channel on NISTube, which is accessible from the NIST internal home page. It will be taken down from NISTube after 12 months at which point it can be requested by emailing the ACMD Seminar Chair.

Abstract: Crystallographic orientations can be measured using Scanning Electron Microscope (SEM) based techniques, such as Electron Backscatter Diffraction (EBSD). The orientation data thus obtained may contain noise and misindexed data. There are several methods to restore the orientation data. The restorations from these methods may have varying levels of quality. Moreover, many such methods are parameter-dependent. Therefore, finding suitable parameter settings for optimal restorations can take time and effort for users of such methods. In this talk, we will discuss an algorithm to obtain high-quality restorations of noisy orientation data and to circumvent the parameter selection problem by automating it. We estimate the noise variance in the data to determine the amount of denoising to apply. We then use this information to determine the stopping criteria for a vector-valued weighted total variation (TV) flow, a nonlinear diffusion applied to the noisy orientation map. We compare the results obtained by our approach with the results from other commonly used denoising filters. As benchmarks, we used simulated EBSD maps with varying noise levels. Our proposed method outperformed denoising methods such as mean, median, spline, half-quadratic, and Kuwahara filters. We will show that the denoising results were statistically significantly better for higher levels of noise.

Bio: Prashant Athavale earned a Ph.D. in Applied Mathematics and Scientific Computation from the University of Maryland, College Park. He is an Assistant Professor at Clarkson University. He has held prior academic positions at Johns Hopkins University, University of Toronto, and UCLA. His current research interests include mathematical image processing and data science.

Host: Gunay Dogan

Note: This talk will be recorded to provide access to NIST staff and associates who could not be present to the time of the seminar. The recording will be made available in the Math channel on NISTube, which is accessible only on the NIST internal network. This recording could be released to the public through a Freedom of Information Act (FOIA) request. Do not discuss or visually present any sensitive (CUI/PII/BII) material. Ensure that no inappropriate material or any minors are contained within the background of any recording. (To facilitate this, we request that cameras of attendees are muted except when asking questions.)

Note: Visitors from outside NIST must contact Meliza Lane at least 24 hours in advance.