Removal of Curtaining Effects by a Variational Model with Directional Forward Differences

TL;DR

This paper introduces a convex variational model that effectively removes curtaining effects from FIB tomography images by splitting the corrupted data into clean and corrupted components using directional differences, improving image quality.

Contribution

A novel infimal convolution variational model utilizing directional first and second order differences for curtaining effect removal in 3D images.

Findings

Effective removal of curtaining effects demonstrated on FIB tomography data.

Model also successfully applied to stripe removal in MODIS satellite images.

Numerical results show superior performance over existing methods.

Abstract

Focused ion beam (FIB) tomography provides high resolution volumetric images on a micro scale. However, due to the physical acquisition process the resulting images are often corrupted by a so-called curtaining or waterfall effect. In this paper, a new convex variational model for removing such effects is proposed. More precisely, an infimal convolution model is applied to split the corrupted 3D image into the clean image and two types of corruptions, namely a striped part and a laminar one. As regularizing terms different direction dependent first and second order differences are used to cope with the specific structure of the corruptions. This generalizes discrete unidirectional total variational (TV) approaches. A minimizer of the model is computed by well-known primal dual techniques. Numerical examples show the very good performance of our new method for artificial and real-world data. Besides FIB tomography, we have also successfully applied our technique for the removal of pure stripes in Moderate Resolution Imaging Spectroradiometer (MODIS) data.