Sparsity based noise removal from low dose scanning electron microscopy images

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.