Enhancing Brain Imaging Using Super Resolution: Challenges and Benefits

Enhancing Brain Imaging Using Super Resolution: Challenges and Benefits

Madhulika Bhadauria, Rishita Khurana, Supavadee Aramvith
Copyright: © 2023 |Pages: 21
DOI: 10.4018/978-1-6684-6980-4.ch004
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Abstract

Super-resolution reconstruction creates one or more high-resolution images from a collection of low-resolution frames. This chapter examines a number of super-resolution methods proposed over the last two decades and provides an overview of the contributions made recently to the broad super-resolution problem. During the procedure, a thorough examination of numerous crucial elements of super-resolution is presented, which are frequently overlooked in the literature. The authors have also outlined various advancements and studies that have been done in the particular domain. The prime focus of this chapter is to highlight the importance and application of super resolution in brain MRI and explore all the work that has been done in the field so far. The experiments on simulated and actual data are used to support novel strategies for tackling the difficulties faced while implementing the technique. Finally, several prospective super-resolution difficulties are identified, and methodologies are presented.
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Literature Review

The main purpose of super-resolution imaging is to reconstruct a high-resolution image based on a series of images acquired from the same scene (referred to as 'low-resolution' images) to avoid image limitations and/or posed to overcome the conditions. An image captures process to facilitate better content visualization and scene recognition. This section provides a comprehensive overview of SR imaging and video reconstruction methods developed in the literature and highlights future research challenges. The SR image approach reconstructs a single high-resolution image from a given set of low-resolution images, while the SR video approach reconstructs a high-resolution image sequence from a group of adjacent low-resolution frames. Additionally, several applications of SR are discussed to provide insightful comments on future SR research directions. A comparison of the prior SR methods used in various studies is provided in Table 1.

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