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12 March 2015 Sparsity based noise removal from low dose scanning electron microscopy images
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Proceedings Volume 9401, Computational Imaging XIII; 940105 (2015) https://doi.org/10.1117/12.2078438
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Lazar and Petru S. Fodor "Sparsity based noise removal from low dose scanning electron microscopy images", Proc. SPIE 9401, Computational Imaging XIII, 940105 (12 March 2015); https://doi.org/10.1117/12.2078438
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