Removing Stripes, Scratches, and Curtaining with Non-Recoverable Compressed Sensing

TL;DR

This paper introduces a compressed sensing-based method using total variation minimization to effectively remove directional artifacts like scratches, stripes, and curtaining from micrographs, even under low signal-to-noise conditions.

Contribution

It presents a novel application of compressed sensing techniques to restore images corrupted by linear artifacts, addressing a key challenge in micrograph interpretation.

Findings

Successfully removes stripe-like artifacts from micrographs.

Robust performance at low signal-to-noise ratios.

Restores images with minimal residual artifacts.

Abstract

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.