Sparse and redundant signal representations for x-ray computed tomography

TL;DR

This paper reviews patch-based image models, highlighting their principles and recent applications in computed tomography, emphasizing their effectiveness amid growing computational resources and radiation safety concerns.

Contribution

It provides a comprehensive overview of patch-based methods and their recent advancements specifically tailored for CT image processing.

Findings

Patch-based models outperform traditional methods in CT tasks

Recent algorithms improve image quality and reduce radiation dose

Patch-based approaches are increasingly adopted in CT imaging

Abstract

Image models are central to all image processing tasks. The great advancements in digital image processing would not have been made possible without powerful models which, themselves, have evolved over time. In the past decade, patch-based models have emerged as one of the most effective models for natural images. Patch-based methods have outperformed other competing methods in many image processing tasks. These developments have come at a time when greater availability of powerful computational resources and growing concerns over the health risks of the ionizing radiation encourage research on image processing algorithms for computed tomography (CT). The goal of this paper is to explain the principles of patch-based methods and to review some of their recent applications in CT. We review the central concepts in patch-based image processing and explain some of the state-of-the-art algorithms, with a focus on aspects that are more relevant to CT. Then, we review some of the recent application of patch-based methods in CT.