Exploration of Medical Image Super-Resolution in terms of Features and Adaptive Optimization

Abstract

Medical image processing takes many steps to capture, process, and convert the images for further analysis. The images are susceptible to distortions due to various factors related to the analysis tools, environment, system-generated faults, and so on. Image enhancement deals with enhancing the quality and resolution of images for accurately analyzing the original information from the images. The primary motivating aspect of research and reconstruction of such high-quality images and their challenges is image super-resolution for image upgrading. This chapter focuses on various image-enhancing strategies in implementing the super-resolution process. In this work, the methodologies of various image-enhancing strategies are explained clearly to provide the parameter selection points, feature comparisons, and performance evaluations that apply to high-resolution image processing. The drawbacks and challenges of each strategy are discussed to investigate the effectiveness of the methodologies. Further research is explored to find hybrid methods on various deep learning architectures to achieve higher accuracy in the field of medical image super-resolution.