
Proceedings Paper
Sparsity based noise removal from low dose scanning electron microscopy imagesFormat | Member Price | Non-Member Price |
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Paper Abstract
Scanning electron microscopes are some of the most versatile tools for imaging materials with nanometer resolution.
However, images collected at high scan rates to increase throughput and avoid sample damage, suffer from low signalto-
noise ratio (SNR) as a result of the Poisson distributed shot noise associated with the electron production and
interaction with the surface imaged. The signal is further degraded by additive white Gaussian noise (AWGN) from the
detection electronics. In this work, denoising frameworks are applied to this type of images, taking advantage of their
sparsity character, along with a methodology for determining the AWGN. A variance stabilization technique is applied
to the raw data followed by a patch-based denoising algorithm. Results are presented both for images with known levels
of mixed Poisson-Gaussian noise, and for raw images. The quality of the image reconstruction is assessed based both on
the PSNR as well as on measures specific to the application of the data collected. These include accurate identification
of objects of interest and structural similarity. High-quality results are recovered from noisy observations collected at
short dwell times that avoid sample damage.
Paper Details
Date Published: 12 March 2015
PDF: 7 pages
Proc. SPIE 9401, Computational Imaging XIII, 940105 (12 March 2015); doi: 10.1117/12.2078438
Published in SPIE Proceedings Vol. 9401:
Computational Imaging XIII
Charles A. Bouman; Ken D. Sauer, Editor(s)
PDF: 7 pages
Proc. SPIE 9401, Computational Imaging XIII, 940105 (12 March 2015); doi: 10.1117/12.2078438
Show Author Affiliations
A. Lazar, Youngstown State Univ. (United States)
Petru S. Fodor, Cleveland State Univ. (United States)
Published in SPIE Proceedings Vol. 9401:
Computational Imaging XIII
Charles A. Bouman; Ken D. Sauer, Editor(s)
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