Removing Stripes, Scratches, and Curtaining with Non-Recoverable Compressed Sensing
This addresses image quality issues in microscopy and tomography for researchers, but it is incremental as it applies existing compressed sensing techniques to a specific artifact removal problem.
The paper tackled the problem of removing directional artifacts like stripes, scratches, and curtaining in micrographs, which degrade image interpretability, by using total variation minimization and compressed sensing to reliably restore images, achieving robustness even at low signal-to-noise.
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.