Direct high-order edge-preserving regularization for tomographic image reconstruction

TL;DR

This paper introduces a novel edge-preserving regularization technique for tomographic image reconstruction that enhances image quality by maintaining sharp edges and reducing noise, outperforming traditional methods like TV regularization.

Contribution

The paper proposes a new two-level iterative algorithm using edge-preserving Laplacian regularization, demonstrating improved reconstruction quality over existing methods.

Findings

Outperforms total variation regularization in image quality

Increases signal-to-noise ratio in reconstructed images

Applicable to under-sampled CT and emission tomography data

Abstract

In this paper we present a new two-level iterative algorithm for tomographic image reconstruction. The algorithm uses a regularization technique, which we call edge-preserving Laplacian, that preserves sharp edges between objects while damping spurious oscillations in the areas where the reconstructed image is smooth. Our numerical simulations demonstrate that the proposed method outperforms total variation (TV) regularization and it is competitive with the combined TV-L2 penalty. Obtained reconstructed images show increased signal-to-noise ratio and visually appealing structural features. Computer implementation and parameter control of the proposed technique is straightforward, which increases the feasibility of it across many tomographic applications. In this paper, we applied our method to the under-sampled computed tomography (CT) projection data and also considered a case of reconstruction in emission tomography The MATLAB code is provided to support obtained results.