  Highly-directional image artifacts such as ion mill curtaining, mechanical
scratches, or image striping from beam instability degrade the interpretability
of micrographs. These unwanted, aperiodic features extend the image along a
primary direction and occupy a small wedge of information in Fourier space.
Deleting this wedge of data replaces stripes, scratches, or curtaining, with
more complex streaking and blurring artifacts-known within the tomography
community as missing wedge artifacts. Here, we overcome this problem by
recovering the missing region using total variation minimization, which
leverages image sparsity based reconstruction techniques-colloquially referred
to as compressed sensing-to reliably restore images corrupted by stripe like
features. Our approach removes beam instability, ion mill curtaining,
mechanical scratches, or any stripe features and remains robust at low
signal-to-noise. The success of this approach is achieved by exploiting
compressed sensings inability to recover directional structures that are highly
localized and missing in Fourier Space.
