High Quality Real-Time Video with Scanning Electron Microscope Using Total Variation Algorithm on a Graphics Processing Unit

Abstract

The scanning electron microscope (SEM) is usually dedicated to taking a picture of micro-nanoscopic objects. In the present study, we wondered whether a SEM can be converted as a real-time video display. To this end, we designed a new methodology. We use the slow mode of the SEM to acquire a high quality reference image that can then be used to estimate the optimal parameters that regularize the signal for a given method. Here, we employ Total Variation, a method which minimizes the noise and regularizes the image. An optimal lagrangian multiplier can be computed that regularizes the image efficiently. We showed that a limited number of iterations for Total Variation algorithm can lead to an acceptable quality of regularization. This algorithm is parallel and deployed on a Graphics Processing Unit to obtain a real-time high quality video with a SEM. It opens the possibility of a real-time interaction at micro-nanoscales.

Recent advances in nanorobotic manipulation inside scanning electron microscopes

“…To circumvent challenges such as SEM image noise and drift, image denoising and drift compensation methods were implemented using graphics processing unit (GPU) techniques 26,127 . SEM tracking algorithms can be classified into feature-based methods, model-based methods and hybrid methods 88,[128][129][130][131][132] .…”

Automated nanoprobing under scanning electron microscopy

“…Other algorithms such as total variation minimization [11] [12] use the calculus of variations for de-noising images. In comparison, the NL-means method [13] is based on globally averaging all the pixels in an image and produces images with lower noise and with more details retained.…”