Strip the Stripes: Artifact Detection and Removal for Scanning Electron Microscopy Imaging

Scanning Electron Microscopy (SEM) is a popular high resolution imaging modality for biological samples that has recently been applied to neural circuit reconstruction. For this application, relatively large volumes are imaged by repeatedly ablating away the exposed surface of the volume with a focused ion beam (FIB), which can cause beam-aligned striping artifacts in images of the remaining layers. We present an automatic pipeline designed to detect and correct for such striping artifacts while minimally degrading the unknown artifact-free image. The proposed method addresses this problem by computing a data-driven mask for the corrupted frequency band and subsequently solving a variational formulation of the image reconstruction problem using efficient methods from convex optimization. Results on simulated and real data show state-of-the-art denoising performance.