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

arXiv: https://arxiv.org/abs/1901.08001 · HTML: https://mlsanity.com/paper/1901.08001/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy](https://mlsanity.com/paper/2206.13419/) (2022)
- [SkelEM: Training-Signal Decoupling of Skeleton and Diffusion for Self-supervised Axial Super-Resolution in Volume Microscopy](https://mlsanity.com/paper/2606.30012/) (2026)

## Abstract (arXiv)

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.